AU724111B2 - System for adaptively filtering audio signals to enhance speech intelligibility in noisy environmental conditions - Google Patents
System for adaptively filtering audio signals to enhance speech intelligibility in noisy environmental conditions Download PDFInfo
- Publication number
- AU724111B2 AU724111B2 AU70784/96A AU7078496A AU724111B2 AU 724111 B2 AU724111 B2 AU 724111B2 AU 70784/96 A AU70784/96 A AU 70784/96A AU 7078496 A AU7078496 A AU 7078496A AU 724111 B2 AU724111 B2 AU 724111B2
- Authority
- AU
- Australia
- Prior art keywords
- noise
- frame
- speech
- filter
- estimate
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Ceased
Links
- 230000005236 sound signal Effects 0.000 title claims description 36
- 238000001914 filtration Methods 0.000 title claims description 12
- 230000007613 environmental effect Effects 0.000 title description 2
- 230000004044 response Effects 0.000 claims description 30
- 238000000034 method Methods 0.000 claims description 23
- 230000001747 exhibiting effect Effects 0.000 claims 1
- 230000006870 function Effects 0.000 description 23
- 230000003595 spectral effect Effects 0.000 description 16
- 238000012545 processing Methods 0.000 description 15
- 230000001413 cellular effect Effects 0.000 description 12
- 230000003044 adaptive effect Effects 0.000 description 9
- 230000009467 reduction Effects 0.000 description 9
- 238000001514 detection method Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000006835 compression Effects 0.000 description 3
- 238000007906 compression Methods 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 235000019800 disodium phosphate Nutrition 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 206010019133 Hangover Diseases 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L21/0216—Noise filtering characterised by the method used for estimating noise
- G10L2021/02168—Noise filtering characterised by the method used for estimating noise the estimation exclusively taking place during speech pauses
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/78—Detection of presence or absence of voice signals
- G10L2025/783—Detection of presence or absence of voice signals based on threshold decision
- G10L2025/786—Adaptive threshold
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L21/0216—Noise filtering characterised by the method used for estimating noise
- G10L21/0232—Processing in the frequency domain
Landscapes
- Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Noise Elimination (AREA)
- Filters That Use Time-Delay Elements (AREA)
- Signal Processing Not Specific To The Method Of Recording And Reproducing (AREA)
- Tone Control, Compression And Expansion, Limiting Amplitude (AREA)
Description
loCk -1- SYSTEM FOR ADAPTIVELY FILTERING AUDIO SIGNALS TO ENHANCE
SPEECH
INTELLIGIBILITY I NOISY ENVIRONMENTAL
CONDITIONS
FIELD OF THE INVENTION The present invention relates to noise reduction systems, and in particular, to an adaptive speech intelligibility enhancement system for use in portable digital radio telephones.
BACKGROUND OF THE INVENTION The cellular telephone industry has made phenomenal strides in commercial operations in the United States AMENDED
SHEET
WO 97/10586 PCT/US96/1 4665 2 as well as the rest of the world. Demand for cellular services in major metropolitan areas is outstripping current system capacity. Assuming this trend continues, cellular telecommunications will reach even the smallest rural markets. Consequently, cellular capacity must be increased while maintaining high quality service at a reasonable cost. One important step towards increasing capacity is the conversion of cellular systems from analog to digital transmission.
This conversion is also important because the first generation of personal communication networks (PCNs), employing low cost, pocket-size, cordless telephones that can be easily carried and used to make or receive calls in the home, office, street, car, etc., will likely be provided by cellular carriers using the next generation digital cellular infrastructure.
Digital communication systems take advantage of powerful digital signal processing techniques. Digital signal processing refers generally to mathematical and other manipulation of digitized signals. For example, after converting (digitizing) an analog signal into digital form, that digital signal may be filtered, amplified, and attenuated using simple mathematical routines in a digital signal processor (DSP).
Typically, DSPs are manufactured as high speed integrated circuits so that data processing operations can be performed essentially in real time. DSPs may also be used to reduce the bit transmission rate of digitized speech which translates into reduced spectral occupancy of the transmitted radio signals and WO 97/10586 PCTIUS96/14665 3 increased system capacity. For example, if speech signals are digitized using 14-bit linear Pulse Code Modulation (PCM) and sampled at an 8 KHz rate, a serial bit rate of 112 Kbits/sec is produced. Moreover, by taking mathematical advantage of redundancies and other predicable characteristics of human speech, voice coding techniques can be used to compress the serial bit rate from 112 Kbits/sec to 7.95 Kbits/sec to achieve a 14:1 reduction in bit transmission rate.
Reduced transmission rates translate into more available bandwidth.
One popular speech compression technique adopted in the United States by the TIA for use as the digital standard for the second generation of cellular telephone systems IS-54) is vector sourcebook excited linear predictive coding (VSELP).
Unfortunately, when audio signals including speech, mixed with high levels of ambient noise (particularly "colored noise"), are coded/compressed using VSELP, undesirable audio signal characteristics may be part of the result. For example, if a digital mobile telephone is used in a noisy environment inside a moving automobile), both ambient noise and desired speech are compressed using the VSELP encoding algorithm and transmitted to a base station where the compressed signal is decoded and reconstituted into audible speech. When the background noise is reconstituted into an analog format, undesirable, audible distortion of the noise, and occasionally in the speech, is WO 97/10586 PCT/US96/14665 4 introduced. This distortion is very annoying to the average listener.
The distortion is caused in large part by the environment in which the mobile telephones are used.
Mobile telephones are typically used in a vehicle's interior where there is often ambient noise produced by the vehicle's engine and surrounding vehicular traffic.
This ambient noise in the vehicle's interior is typically concentrated in the low audible frequency range and the magnitude of the noise can vary due to such factors as the speed and acceleration of the vehicle and the extent of the surrounding vehicular traffic. This type of low frequency noise also has the tendency of significantly decreasing the intelligibility of the speech coming from the speaking person in the car environment. The decrease in speech intelligibility caused by low frequency noise can be particularly significant in communication systems deploying a VSELP vocoder, but can also occur in communication systems that do not include a VSELP vocoder.
The influence of the ambient noise on the mobile telephone can also be affected by the manner in which the mobile telephone is used. In particular, the mobile telephone may be used in a hands-free mode where the telephone user talks on the telephone while the mobile telephone is in a cradle. This frees the telephone user's hands to drive but also increases the distance that the telephone user's audible words must travel before reaching the microphone input of the 11( 8 mobile telephone. This increased distance between the user and the mobile telephone, along with the varying ambient noise, can result in noise being a significant portion of the total power spectral energy of the audio signal inputted into the mobile telephone.
Prior art disclosure contained in EP 0 645 756, EP 0 558 312, EP 0 665 530, DE 4 012 349, U.S. Patent Nos.
4,811,404, 4,461,025, and 5,251,263 all disclose manners by which to filter unwanted signal components.
In theory, various signal processing algorithms could be implemented using digital signal processors to filter the VSELP encoded background noise. These solutions, however, often require significant digital signal processing overhead, measured in terms of millions of instructions executed per second (MIPS), which consumes valuable processing time, memory space, and power consumption. Each of these signal processing resources, however, ,is limited in portable radiotelephones. Hence, simply increasing the processing burden of the DSP is not an optimal solution for minimizing VSELP encoded and other types of background noise.
SUMMARY OF THE INVENTION The present invention provides an adaptive noise reduction system that reduces the undesirable contributions of encoded background noise while both minimizing any negative impact on the quality of the encoded speech and minimizing any increased drain on digital signal processor resources. The method and system of the present invention increases the intelligibility of the speech in a digitized, audio signal by passing frames of the digitized audio signal through a filter circuit.
The filter circuit functions A I I L 7 1 E Z; WO 97/10586 PCT/US96/14665 6 as an adjustable, high-pass filter which filters a portion of the digitized signal in a low audible frequency range and passes the portion of the digitized signal falling in higher frequency ranges. Because the noise in a vehicle tends to be concentr.td in a low audible frequency range and only a relatively small portion of the intelligibility content of speech falls within this low frequency range, the filter circuit filters a large segment of the noise in the digitized audio signal while only filtering less important segments of the speech. This results in a relatively larger portion of the noise energy being removed compared to the portion of the speech energy removed.
By adaptively adjusting and selecting the frequency response curve of the filter circuit, the amount of speech filtered is limited and has a minimal affect on the intelligibility of the speech outputted by the radio.
A filter control circuit is used to adjust the filter circuit to exhibit different frequency response curves as a function of a noise estimate and/or a spectral profile result corresponding to the noise in the audio signal. The noise estimate and/or the spectral profile result are adjusted on a frame-byframe basis for the digital signal and as a function of speech detection. If speech is not detected, the noise estimate and/or spectral profile result is updated for the current frame. If speech is detected, the noise estimate and/or spectral profile result is left unadjusted.
1 WO 97/10586 PCT/US96/14665 7 In a first embodiment, the filter circuit calculates noise estimates for the frames of the digitized audio signals. The noise estimates correspond to the amount of background noise in the frames of the digitized audio signals. As the relativre.: amount of background noise to speech in a low frequency range of speech increases, the noise estimates increase. The filter control circuit uses the noise estimates to adjust the filter circuit to filter larger portions of the low frequency range of speech as the relative amount of background noise to speech in a low frequency range of speech increases. When no background noise is present, no portion of the speech signal is filtered. Larger portions of noise and speech information are extracted when there is a higher level of background noise. Because noise tends to be concentrated in a low frequency range and only a relatively small portion of the intelligibility content of speech falls within this low frequency range, the overall intelligibility of the audio signal can be increased by increasing the portion of low frequency energy being filtered as the noise estimates increase.
In a second embodiment, a modified filter control circuit is used to adjust the filter circuit to exhibit different frequency response curves as a function of a noise profile of the noise estimate over a selected frequency range in the audio signal. The filter control circuit includes a spectral analyzer for determining a noise profile estimate as a function of the detection speech. A noise profile estimate is WO 97/10586 PCT/US96/14665 8 determined for a current frame and compared to a reference noise profile. Based on this comparison, the filter circuit is adaptively adjusted to extract varying amounts of low frequency energy from the current frame.
The adaptive noise reduction system according to the present invention may be advantageously applied to telecommunication systems in which portable/mobile radio transceivers communicate over RF channels with each other or with fixed telephone line subscribers.
Each transceiver includes an antenna, a receiver for converting radio signals received over an RF channel via the antenna into analog audio signals, and a transmitter. The transmitter includes a coder-decoder (codec) for digitizing analog audio signals to be transmitted into frames of digitized speech information, the speech information including both speech and background noise. A digital signal processor processes a current frame based on an estimate of the background noise and the detection of speech in the current frame to minimize background noise. A modulator modulates an RF carrier with the processed frame of digitized speech information for subsequent transmission via the antenna.
BRIEF DESCRIPTION OF THE DRAWINGS These and other features and advantages of the present invention will be readily apparent to one of ordinary skill in the art from the following written WO 97/10586 PCT/US96/14665 9 description, read in conjunction with the drawings, in which: FIGURE 1 is a general functional block diagram of the present invention; FIGURE 2 i.l!ustrates the frame and slot structure of the U.S. digital standard IS-54 for cellular radio communications; FIGURE 3 is a block diagram of a first preferred embodiment of the present invention implemented using a digital signal processor; FIGURE 4 is a functional block diagram of an exemplary embodiment of the present invention in one of plural portable radio transceivers in a telecommunication system; FIGURES 5A and 5B is a flow chart which illustrates functions/operations performed by the digital signal processor in implementing the first preferred embodiment of the present invention; FIGURE 6A is a graph illustrating a first example of an attenuation vs. frequency characteristic of a filter circuit according to the first preferred embodiment of the present invention; FIGURE 6B is a graph illustrating a second example of an attenuation vs. frequency characteristic of a filter circuit according to the first preferred embodiment of the present invention; FIGURE 7 is an example look-up table accessible by the filter control circuit of the first preferred embodiment of the present invention; WO 97/10586 PCT/US96/14665 FIGURES 8A and 8B are graphs illustrating the amplitude vs. frequency characteristics of example input audio signals; FIGURES 9A and 9B are graphs illustrating the amplitude vs. frequency charnce-ristics of the input audio signals of Figures 8A and 8B, respectively, after having been filtered by the filter circuit of the present invention; FIGURE 10 is a block diagram of a second preferred embodiment of the present invention implemented using a digital signal processor; FIGURE 11 is a flow chart, corresponding to the flow chart of Figure 5B, which illustrates functions/operations performed by the digital signal processor in implementing the second preferred embodiment of the present invention; and FIGURE 12 is an example look-up table accessible by the filter control circuit of the second preferred embodiment of the present invention.
DETAILED DESCRIPTION OF THE DRAWINGS In the following description, for purposes of explanation and not limitation, specific details are, set forth, such as particular circuits, circuit components, techniques, flow charts, etc. in order to provide a thorough understanding of the invention.
However, it will be apparent to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details.
In other instances, detailed descriptions of well known WO 97/10586 PCT/US96/14665 11 methods, devices, and circuits are omitted so as not to obscure the description of the present invention with unnecessary details.
Figure 1 is a general block diagram of the adaptive noise reduction system 100 according to the present invention. Adaptive noise reduction system 100 includes a filter control circuit 105 connected to a filter circuit 115. Filter control circuit 105 generates a filter control signal for a current frame of a digitized audio signal. The filter control signal is outputted to the filter circuit 115, and the filter circuit 115 adjusts in response to the filter control signal to exhibit a high-pass frequency response curve selected based on the filter control signal. The adjusted filter circuit 115 filters the current frame of the digitized audio signal. The filtering signal is processed by a voice coder 120 to produce a coded signal representing the digitized audio signal.
In an exemplary embodiment of the invention applied to portable/mobile radio telephone transceivers in a cellular telecommunications system, Figure 2 illustrates the time division multiple access (TDMA) frame structure employed by the IS-54 standard for digital cellular telecommunications. A "frame" is a twenty millisecond time period which includes one transmit block TX, one receive block RX, and a signal strength measurement block used for mobile-assisted hand-off (MAHO). The two consecutive frames shown in Figure 2 are transmitted in a forty millisecond time period. Digitized speech and background noise WO 97/10586 PCT/US96/14665 12 information is processed and filtered on a frame-byframe basis as further described below.
Preferably, the functions of the filter control circuit 105, filter circuit 115, and voice coder 120 shown in Figure 1 are implemented with a high speed digital signal processor. One suitable digital signal processor is the TMS320C53 DSP available from Texas Instruments. The TMS320C53 DSP includes on a single integrated chip a sixteen-bit microprocessor, on-chip RAM for storing data such as speech frames to be processed, ROM for storing various data processing algorithms including the VSELP speech compression algorithm, and other algorithms to be described below for implementing the functions performed by the filter control circuit 105 and the filter circuit 115.
A first embodiment of the present invention is shown in Figure 3. In the first embodiment, the filter circuit 115 is adjusted as a function of background noise estimates determined by the filter control circuit. Frames of pulse code modulated (PCM) audio information are sequentially stored in the DSP's onchip RAM. The audio information could be digitized using other digitization techniques. Each PCM frame is retrieved from a DSP on-chip RAM and processed by frame energy estimator 210, and stored temporarily in temporary frame store 220. The energy of the current frame determined by frame energy estimator 210 is provided to noise estimator 230 and speech detector 240 function blocks. Speech detector 240 indicates that speech is present in the current frame when the frame WO 97/10586 PCT/US96/14665 13 energy estimate exceeds the sum of the previous noise estimate and a speech threshold. If the speech detector 240 determines that no speech is present, the digital signal processor 200 calculates an updated noise estimate as a function of the previous noise estimate and the current frame energy (block 230).
The updated noise estimate is outputted to a filter selector 235. Filter selector 235 generates a filter control signal based on the noise estimate. In the preferred embodiment, the filter selector 235 accesses a look-up table in generating the filter control signal. The look-up table includes a series of filter control values that are each matched with a noise estimate or range of noise estimates. A filter control value from a look-up table is selected based on the updated noise estimate and this filter control value is represented by a filter control signal outputted to a filter bank 265 for the filter circuit 115. To stabilize the process and avoid accessive switching between different filters a hangover time of N frames is set upon the selection of a new filter. A new filter can only be selected every N frames, where N is an integer greater than one and preferably greater than The filter circuit 115 is adjusted in response to the filter control signal to exhibit a high-pass frequency response curve that corresponds with the inputted filter control signal and noise estimate.
Various different types of filter circuits well known in prior art can be utilized to exhibit selected -14frequency response curves in response to the filter control signal. These prior art filters include IIR filters such as Butterworth, Chebyshev (Tschebyscheff) or elliptic filters. IIR filters are preferable to FIR filters, which also can be used, due to lower processing requirements.
The filtered signal is processed by a voice coder 120 which is used to compress the bit rate of the filtered signal. In the preferred embodiments, the voice coder 120 uses vector sourcebook excited linear predictive coding (VSELP) to code the audio signal. Other voice coding techniques and algorithms such as code excited linear predictive (CELP) codings, residual pulse excited linear predictive (RPE-LTP) coding, improved multiband excited (IMBE) coding can be used. By filtering the frames of audio signals in accordance with the present invention before voice coding, background noise is minimized which substantially reduces any undesired noise effects in the speech when it is reconstituted. It also prevents the speech from being "drowned" in low frequent noise.
The digital signal processor 200 described in conjunction with Figure 3 can be used, for example, in the transceiver of a digital portable/mobile radiotelephone used in a radio telecommunications system. Figure 4 illustrates one such digital radio transceiver which may be used in a cellular telecommunications network.
AMENDED SHEET Audio signals including speech and background noise are input in a microphone 400 to a coder-decoder (codec) 402 which preferably is an application specific integrated circuit (ASIC). The band limited audio signals detected at microphone 400 are sampled by the codec 402 at a rate of 8,000 samples per second and blocked into frames.
Accordingly, each twenty millisecond frame includes 160 speech samples. These samples are quantized and converted into a coded digital format such as 14-bit linear PCM.
Once 160 samples of digitized speech for a current frame are stored in a transmit DSP 200 in on-chip RAM 202, the transmit DSP 200 performs channel encoding functions, the frame energy estimation, noise estimation, speech detection, FFT, filter functions and digital speech coding/compression in accordance with the VSELP algorithm, as described above in conjunction with Figure 3.
A supervisory microprocessor 432 controls the overall operation of all of the components in the transceiver shown in Figure 4. The filtered PCM data stream generated by transmit DSP 200 is provided for quadrature modulation and transmission. To this end, an ASIC gate array 404 generates in-phase and quadrature channels of information based upon the filtered PCM data stream from DSP 200. The I and Q bit ,E-iDED SHEET WO 97/10586 PCT/US96/14665 16 streams are processed by matched, low pass filters 406 and 408 and passed onto IQ mixers in balanced modulator 410. A reference oscillator 412 and a multiplier 414 provide a transmit intermediate frequency The I signal is mixed with in-phase IF, and the Q Cignal is mixed with quadrature IF the in-phase IF delayed by 90 degrees by phase shifter 416). The mixed I and 0 signals are summed, converted "up" to an RF channel frequency selected by channel synthesizer 430, and transmitted via duplexer 420 and antenna 422 over the selected radio frequency channel.
On the receive side, signals received via antenna 422 and duplexer 420 are down converted from the selected receive channel frequency in a mixer 424 to a first IF frequency using a local oscillator signal synthesized by channel synthesizer 430 based on the output of reference oscillator 428. The output of the first IF mixer 424 is filtered and down converted in frequency to a second IF frequency based on another output from channel synthesizer 430 and demodulator 426. A receive gate array 434 then converts the second IF signal into a series of phase samples and a series of frequency samples. The receive DSP 436 performs demodulation, filtering, gain/attenuation, channel decoding, and speech expansion on the received signals.
The processed speech data are then sent to codec 402 and converted to baseband audio signals for driving loudspeaker 438.
The operations performed by the digital signal processor 200 for implementing the functions of filter WO 97/10586 PCT/US96/14665 17 control circuit 105, filter circuit 115, and voice coder 120 will now be described in conjunction with the flow chart illustrated in Figures 5A and 5B. Frame energy estimator 210 determines the energy in each frame of audio signals. Frame energy estimator 210 determines the energy of the current frame by calculating the sum of the squared values of each PCM sample in the frame (step 505). Since there are 160 samples per twenty millisecond frame for an 8000 samples per second sampling rate, 160 squared PCM samples are summed. Expressed mathematically, the frame energy estimate is determined according to equation 1 below: 160 Frame energy 7{Samp(i))2 (equation 1) i-1 The frame energy value calculated for the current frame is stored in the on-chip RAM 202 of DSP 200 (step 510) The functions of speech detector 240 include fetching a noise estimate previously determined by noise estimator 230 from the on-chip RAM of DSP 200 (step 515). Of course, when the transceiver is initially powered up, no noise estimate will exist.
Decision block 520 anticipates this situation and assigns a noise estimate in step 525. Preferably, an arbitrarily high value, e.g. 20 dB above normal speech levels, is assigned as the noise estimate in order to force an update of the noise estimate value as will be described below. The frame energy determined by frame WO 97/10586 PCT/US96/14665 18 energy estimator 210 is retrieved from the on-chip RAM 202 of DSP 200 (block 530). A decision is made in block 535 as to whether the frame energy estimate exceeds the sum of the retrieved noise estimate plus a predetermined speech threshold value, as shown in equation 2 below: frame energy estimate (noise estimate speech threshold) (equation 2) The speech threshold value may be a fixed value determined empirically to be larger than short term energy variations of typical background noise and may, for example, be set to 9 dB. In addition, the speech threshold value may be adaptively modified to reflect changing speech conditions such as when the speaker enters a noisier or quieter environment. If the frame energy estimate exceeds the sum in equation 2, a flag is set in block 570 that speech exists. If speech detector 240 detects that speech exists, then noise estimator 230 is bypassed and the noise estimate calculated for the previous frame in the digitized audio is retrieved and used as the current noise estimate. Conversely, if the frame energy estimate is less than the sum in equation 2, the speech flag is reset in block 540.
Other systems for detecting speech in a current frame can also be used. For example, the European Telecommunications Standards Institute (ETSI) has developed a standard for voice activity detection (VAD) in the Global System for Mobile communications (GSM) system and is described in the ETSI Reference: RE/SMG- WO 97/10586 PCT/US96/14665 19 020632P which is incorporated by reference. This standard could be used for speech detection in the present invention and is incorporated by reference.
If speech does not exist, the noise estimation update routine of:noise.. estimator 230 is executed. In essence, the noise estimate is a running average of the frame energy during periods of no speech. As described above, if the initial start-up noise estimate is chosen sufficiently high, speech is not detected, and the speech flag will be reset thereby forcing an update of the noise estimate.
In the noise estimation routine followed by noise estimator 230, a difference/error delta is determined in block 545 between the frame noise energy generated by frame energy estimator 210 and a noise estimate previously calculated by noise estimator 230 in accordance with the following equation: A current frame energy previous noise estimate (equation 3) A determination is made in decision block 550 whether A exceeds zero. If A is negative, as occurs for high values of the noise estimate, then the noise estimate is recalculated in block 560 in accordance with the following equation: noise estimate previous noise estimate A/2 (equation 4) Since A is negative, this results in a downward correction of the noise estimate. The relatively large step size of A/2 is chosen to rapidly correct for WO 97/10586 PCT/US96/14665 decreasing noise levels. However, if the frame energy exceeds the noise estimate, providing a A greater than zero, the noise is updated in block 555 in accordance with the following equation: noise estimate previous noiJe estimate A/256 (equation Since A is positive, the noise estimate must be increased. However, a smaller step size of A/256 (as compared to A/2) is chosen to gradually increase the noise estimate and provide substantial immunity to transient noise.
The noise estimate calculated for the current frame is outputted to the filter selector 235. In the first preferred embodiment, filter selector 235 accesses a look-up table and uses the current noise estimate to select a filter control value (Step 572). The filter circuit 115 (in Step 574) is then adjusted as a function of the selected filter control value to exhibit a frequency response curve intended to increase the amount of noise filtered as the noise estimate and background noise increases. The PCM samples stored in DSP RAM are then passed through the adjusted filter circuit 265 to filter the PCM samples in order to remove noise (Step 576). The filtered PCM samples are then processed by voice coder 120 (step 578), and the coded samples are then outputted to RF transmit circuits (Step 580) Figures 6A and 6B show examples of how the filter circuit 115 adjusts to exhibit different frequency response curves F1-F4 for different filter control WO 97/10586 PCT/US96/14665 21 signals inputted to the filter circuit 115. As shown in Figure 6A, the filter circuit 115 can be selected to exhibit a series of different frequency response curves with the frequency response curves F1-F4 having cut-off frequencies Flc-F4c, respectively. The cut-off frequencies of filter circuit 115 may range in the preferred embodiment from 300 Hz to 800 Hz. As the noise estimates increase, the filter circuit 115 is designed to exhibit frequency response curves having higher cut-off frequencies. The higher cut-off frequencies result in a larger portion of frame energy falling within the lower frequency range of speech being extracted by the filter circuit 115.
Likewise, as shown in Figure 6B, the filter circuit 115 can be selected to exhibit a series of different frequency response curves F1-F4 with each frequency response curve having a different slope and the same cut-off frequencies. The cut-off frequency for frequency response curves FI-F4 is in the abovementioned range. As the noise estimate increases, the filter circuit 115 is adjusted to exhibit frequency response curves having steeper slopes. The steeper slopes result in a larger portion of frame energy falling within the lower frequency range of speech being extracted by the filter circuit 115.
The filter circuit 115 filters the current frames as a function of the noise estimate calculated for the current frame. The current frame is filtered so that the noise is reduced and a major portion of the speech is passed. The major portion of speech which is passed WO 97/10586 PCTIUS96/14665 22 unfiltered provides for recognizable speech output with only a minimal reduction in the quality of the speech signal. A combination of different cutoff frequencies and different slopes could be used for adaptively extracting selected portions of frame energy falling within a low frequency range of speech.
Figure 7 depicts an example look-up table accessed by filter selector 235 in order to select one of the filter response curves F1-F4 for filter circuit 115.
The look-up table includes a series of potential noise estimates N1-Nn and filter control values F1-Fn that correspond with potential response curves that are exhibitable by the filter circuit 115. Noise estimates N1-Nn can each represent a range of noise estimates and are each matched with a particular filter control value F1-F4. The filter control circuit 105 generates a filter control signal by calculating a noise estimate and retrieving from the look-up table the filter control value associated therewith.
Figures 8A B and 9A B show how the audio signal for two frames are each adaptively filtered to provide an improved audio signal outputted to the RF transmitter. Figures 8A and 8B show a first frame and a second frame of an audio signal containing speech components s1 and s2 and noise components nl and n2, respectively. As shown, the noise energy nl and n2 in both frames is concentrated in a low audible frequency range, while the speech energy si and s2 is concentrated in a higher audible frequency range. Figure 9A shows the noise signal nl and speech signal si for the first WO 97/10586 PCT/US96/14665 23 frame after filtering. Figure 9B shows the noise signal n2 and speech signal s2 for the second frame after filtering.
The adaptive audio noise reduction system i00, as discussed, is designed to account for the difference in noise level between the first frame and the second frame by adjusting the filter control circuit 105 based on a calculated noise estimate for the current frame. For example, a noise estimate N1 and a spectral profile Si is calculated by filter control circuit 105 and a filter control value of Fl is selected for the first frame. In the preferred embodiment, the filter circuit 115 is adjusted based on filter control value Fl and exhibits a frequency response curve Fl having a cut-off frequency Flc, as shown in Figure 6A. The first frame is passed through this adjusted filter circuit 115. The filter circuit 115 is selected so that a large portion of the noise nl and only a small portion of speech si falls below the cut-off frequency Flc of the frequency response curve Fl. This results in noise nl being effectively filtered and only a relatively insignificant portion of speech si being filtered. The filtered audio signal of the first frame is shown in Figure 9A.
In the second frame shown in Figure 8b, a higher background noise is present, and assuming speech is not detected, a higher noise estimate n2 is calculated by filter control circuit 105. A higher corresponding filter control value F2 is determined for the second frame based on the higher noise estimate. In the first preferred embodiment, the filter circuit 115 is adjusted WO 97/10586 PCTIUS96/14665 24 in response to the higher filter control value F2 to exhibit a frequency response curve having a higher cutoff frequency F2c, as shown in Figure 6A. The subsequent frame of audio signal is passed through the adjusted filter circuit 115. Because the cut-off frequency F2c of the frequency response curve F2 is higher for the subsequent frame, a larger portion of both the noise n2 and speech s2 is filtered. The portion of speech s2 filtered is still relatively insignificant to the intelligibility information contained by the frame so that there is only minimal affect on the speech. The disadvantage of filtering a larger portion of the speech s2 is offset by the advantage of the increased removal of noise n2 from the second frame. The filtered spectral portion of the speech does not significantly contribute to the intelligibility of the speech. The filtered audio signal of the second frame is shown in Figure 9B.
A second preferred embodiment of adaptive noise reduction system 100 is shown in Figures 10-12. In the second preferred embodiment, the filter control circuit 105 adjusts the filter circuit 115 as a function of noise profile estimates. A noise profile estimate is calculated for each frame and is compared to a reference noise profile. Based on this comparison, the filter circuit 115 is adaptively adjusted to extract varying amounts of low frequency energy from the current frame.
Referring to Figure 10, a DSP 200 configured according to the second preferred embodiment is shown.
As shown, the filter control circuit 105 includes a WO 97/10586 PCTIUS96/1 4665 spectral analyzer 270, in addition to frame energy estimator 210, noise estimator 230, speech detector 240, and filter selector 235 which are described with respect to the first preferred embodiment. The filter control circuit 105 determines noise estimates and detects speech for the received frames as described for the first embodiment and shown in flow charts 5A and Upon speech detection for a current frame, the spectral analyzer 270 updates the noise profile estimate and uses the noise profile estimate in adjusting the filter circuit 115.
Referring to Figure 11, the steps of updating the noise profile estimate and adjusting the filter circuit 115 is shown. Figure 11 shows the steps performed by spectral analyzer 270 incorporated into the overall process previously described in the flow charts of Figures 5A and SB for the first preferred embodiment.
When speech is not detected for the current frame, the spectral analyzer 270 first determines a noise profile for the current frame (step 600). The noise profile determined for the current frame includes energy calculations for different frequencies frequency bins) within a selected low frequency range of speech for the current frame. In the preferred embodiment, the selected frequency range is approximately 300 to 800 hertz. The noise profile of the current frame can be determined by processing the current frame using a Fast Fourier Transform
(FFT)
having N frequency bins. Processing digital signals using an FFT is well-known in the prior art and is WO 97/10586 PCT/US96/14665 26 advantageous in that very little processing power is required where the FFT is limited to a relatively small number of frequency bins such as 32. An FFT having
N
frequency bins produces energy calculations at N different frequencies. The energy calculations for the frequency bins falling within the selected frequency range form the noise profile for the current frame.
To determine the noise profile estimate for the current frame (step 604), the noise profile for the current frame is averaged with a noise profile estimate determined for the previous frame of the audio signal.
Where no previous noise profile estimate is available, such as after initialization, a stored, initial noise profile estimate can be used. The noise profile estimate includes noise energy estimates e i (where i located at successively lower frequencies el is the noise energy estimate for the highest frequency and e n is the noise energy estimate for the lowest frequency in the selected frequency range). In the preferred embodiment, each noise energy estimate ei corresponds to an average of the energy calculations at a particular frequency in the selected frequency range over a plurality of successive frames in which no speech was detected. By using a plurality of frames in determining the noise profile estimate, the filter circuit 115 is adjusted on a more gradual basis. In alternate embodiments, the noise profile estimate can be equated to the noise profile of the current frame.
The energy estimates e i of the noise profile estimate are then compared with a reference noise WO 97/10586 PCT/US96/14665 27 profile (step 604). The reference noise profile includes reference energy thresholds en, (where i at frequencies corresponding to the frequencies for noise energy estimates e, of the noise profile estimate. The reference energy thresholds er, can be determined empirically. The noise energy estimates e i are successively compared to corresponding reference energy thresholds erj from the highest frequency energy estimate e, to the lowest frequency energy estimate en.
More specifically, noise energy estimate el is first compared to reference noise threshold erl. If e, is greater than reference noise threshold eri, then a comparison value cl is selected and inputted into filter selector 235. If noise energy estimate e, is less than reference noise threshold eri, then noise energy estimate e 2 (which is a noise energy estimate taken at a lower frequency than is compared to reference noise threshold er2. If noise energy estimate e 2 is greater than reference noise threshold er2, then a comparison value c 2 is selected and inputted to filter selector 235. This comparison process is continued until a comparison value c i (where i is selected.
The filter circuit 235 uses the determined comparison value c i to determine a filter control value.
The filter control value is selected from a look-up table such as that shown in Figure 12. The look-up table includes a series of comparison values c i and WO 97/10586 PCT/US96/14665 28 corresponding filter control values
F
i The filter circuit 115 is adjusted as a function of the selected filter control value. The filter circuit 115 is adjusted to exhibit a frequency response curve for extracting low frequency energy from the current frame.
The filter circuit 115 is adjusted to extract increasing amounts of low frequency energy as noise energy estimates at successively higher frequencies surpass their corresponding reference energy thresholds. Figure 6A and 6B show example frequency response curves for selected filter control values.
Use of noise profile estimates helps improve the ability to adaptively adjust the filter circuit to extract low frequency energy in a manner to improve the overall quality of speech. Since the car environment is not the only environment where a mobile telecommunications device is used, and therefore the noise profile in certain situations could be tilted more towards higher frequencies, the spectral analyzer 270 can be selectively disabled when noise energy in the low frequencies is small. Also, when a significant portion of the noise frequency spectrum resides in lower frequencies a steeper filtering slope could be applied even though some processing power may be sacrificed. This extra processing requirement is still fairly small.
As is evident from the description above, the adaptive noise filter system of the present invention is implemented simply and without significant increase in DSP calculations. More complex methods of reducing -29noise, such as "spectral subtraction," require several calculation-relates MIPS and a large amount of memory for data and program code storage. By comparison, the present invention may be implemented using only a fraction of the MIPS and memory required for the "spectral subtraction" algorithm which also introduces more speech distortion.
Reduced memory reduces the size of the DSP integrated circuits; decreased MIPS decreases power consumption.
Both of these attributes are desirable for battery-powered portable/mobile radiotelephones.
While the invention has been particularly shown and described with reference to the preferred embodiments thereof, it is not limited to those embodiments. For example, although a DSP is disclosed as performing the functions of the frame energy estimator 210, noise estimator 230, speech detector 240, filter selector 235 and filter circuit 265, these functions could be implemented using other digital and/or analog components.
In addition, an adaptive filtering system 100 could be implemented where the filter circuit 115 is adjusted as a function of both noise estimates and noise profile estimates.
Th 6';t~
Claims (12)
1. A method for selectively altering a frame of a digital signal formed of a plurality of successive frames, the digital signal representative of an audio signal received at a transmitter, the audio signal formed alternately of a speech component, a noise component, and the speech component together with the noise component, said method being characterized by the steps of: estimating an energy level of a frame of the digital signal; determining, responsive to the estimate made during said step of estimating, whether the frame of the digital signal includes a speech component; updating a noise estimate as a function of a preceding noise estimate and the energy level estimated during said step of estimating when said step of determining does not contain a speech component; accessing an entry in a look-up table having filter characteristics indexed against levels of noise estimates, the entry accessed associated with the noise estimate updated during said step of updating; selecting filter characteristics for a filter such that e the filter exhibits a frequency response curve having variable gain over different frequency ranges, the filter characteristic selected responsive to the stored filter characteristics of the entry accessed during said step of accessing; and filtering the frame of the digital data with the filter which exhibits the filter characteristics, thereby to alter the frame of the digital data responsive to the filter characteristics.
2. The method of claim 1 further characterized by the additional intermediary step of determining a noise profile estimate of the frame of the digital signal if the frame of R the digital data is determined not to include the speech Somponent.
3. The method of claim 2 wherein the noise profile estimate determined during said step of determining the noise profile estimate is used during said step of updating to update the noise estimate.
4. The method of claim 1 wherein the look-up table accessed during said step of accessing is characterized by a plurality of entries, each entry of the plurality including a separate filter characteristic.
The method of claim 4 wherein the separate filter characteristics of the plurality of entries of the look-up table include separate high pass filter characteristics, each high pass characteristic defined by a separate cut-off frequency.
6. The method of claim 4 wherein the separate filter characteristics of the plurality of entries of the look-up table include separate high pass filter characteristics, each high pass filter characteristic defined by a separate o frequency response curve slope. t
7. The method of claim 1 characterized by the further step of incrementing a counter value to count each frame for which an energy level is estimated during said step of estimating.
8. The method of claim 7 wherein said step of selecting the filter-circuit filter characteristics is performed when S 'the counter value is incremented each Nth time, N forming an integer value greater than one.
9. An apparatus for selectively altering a frame of a digital signal formed of a plurality of successive frames, the digital signal representative of an audio signal received at a transmitter, the audio signal formed alternately of a speech component, a noise component, and the speech component together with the noise component, said apparatus characterized by: an energy level estimator coupled to receive indications of a frame of the digital signal, said energy level estimator for estimating an energy level of the frame of the digital signal; a speech detector coupled to said energy level estimator, said speech detector for determining whether the frame of the digital signal includes a speech component; a noise estimator operable when said speech detector determines that a frame does not contain a speech component, said noise estimator for updating a noise estimate as a function of a preceding noise estimate, and the energy level estimated by said estimator; a look-up table containing a plurality of entries, each entry indexed against levels of noise estimates, an entry of said look-up table accessed responsive to a noise estimate formed by said noise estimator; and a filter coupled to receive the frame of the digital data, said filter exhibiting selectable filter characteristics enabling the filter to exhibit a frequency response curve having variable gain over different frequency ranges, selection of the filer characteristics of the filter determined responsive to the entry of the look-up table accessed responsive to the noise estimate updated by said noise estimator.
The apparatus of claim 9 further characterized by a noise profile estimator for determining a noise profile estimate of the frame of the digital data if the frame of the digital data is determined by said speech component determiner not to include the speech component.
11. A method as claimed in claim 1 substantially as herein described with reference to the accompanying drawings. 33
12. An apparatus as claimed in claim 9 substantially as herein described with reference to the accompanying drawings. DATED this llth day of July 2000 ERICSSON INC. WATERMARK PATENT TRADEMARK ATTORNEYS 290 BURWOOD ROAD HAWTHORN VICTORIA 3122 AUSTRALIA P13008AU00 RCS:MBP:SLB *tee p** 9 oo*o
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US52800595A | 1995-09-14 | 1995-09-14 | |
US08/528005 | 1995-09-14 | ||
PCT/US1996/014665 WO1997010586A1 (en) | 1995-09-14 | 1996-09-13 | System for adaptively filtering audio signals to enhance speech intelligibility in noisy environmental conditions |
Publications (2)
Publication Number | Publication Date |
---|---|
AU7078496A AU7078496A (en) | 1997-04-01 |
AU724111B2 true AU724111B2 (en) | 2000-09-14 |
Family
ID=24103874
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
AU70784/96A Ceased AU724111B2 (en) | 1995-09-14 | 1996-09-13 | System for adaptively filtering audio signals to enhance speech intelligibility in noisy environmental conditions |
Country Status (15)
Country | Link |
---|---|
EP (1) | EP0852052B1 (en) |
JP (1) | JPH11514453A (en) |
KR (1) | KR100423029B1 (en) |
CN (1) | CN1121684C (en) |
AU (1) | AU724111B2 (en) |
BR (1) | BR9610290A (en) |
CA (1) | CA2231107A1 (en) |
DE (1) | DE69613380D1 (en) |
EE (1) | EE03456B1 (en) |
MX (1) | MX9801857A (en) |
NO (1) | NO981074L (en) |
PL (1) | PL185513B1 (en) |
RU (1) | RU2163032C2 (en) |
TR (1) | TR199800475T1 (en) |
WO (1) | WO1997010586A1 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10249317B2 (en) | 2014-07-28 | 2019-04-02 | Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. | Estimating noise of an audio signal in a LOG2-domain |
Families Citing this family (170)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE19747885B4 (en) * | 1997-10-30 | 2009-04-23 | Harman Becker Automotive Systems Gmbh | Method for reducing interference of acoustic signals by means of the adaptive filter method of spectral subtraction |
JP2001508197A (en) * | 1997-10-31 | 2001-06-19 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | Method and apparatus for audio reproduction of speech encoded according to the LPC principle by adding noise to a constituent signal |
KR20000074236A (en) * | 1999-05-19 | 2000-12-15 | 정몽규 | Auto audio volume control means |
US8645137B2 (en) | 2000-03-16 | 2014-02-04 | Apple Inc. | Fast, language-independent method for user authentication by voice |
JP2001318694A (en) * | 2000-05-10 | 2001-11-16 | Toshiba Corp | Device and method for signal processing and recording medium |
US6983242B1 (en) * | 2000-08-21 | 2006-01-03 | Mindspeed Technologies, Inc. | Method for robust classification in speech coding |
KR20030010432A (en) * | 2001-07-28 | 2003-02-05 | 주식회사 엑스텔테크놀러지 | Apparatus for speech recognition in noisy environment |
IL148592A0 (en) | 2002-03-10 | 2002-09-12 | Ycd Multimedia Ltd | Dynamic normalizing |
KR100978015B1 (en) * | 2002-07-01 | 2010-08-25 | 코닌클리케 필립스 일렉트로닉스 엔.브이. | Stationary spectral power dependent audio enhancement system |
WO2004004297A2 (en) * | 2002-07-01 | 2004-01-08 | Koninklijke Philips Electronics N.V. | Stationary spectral power dependent audio enhancement system |
WO2004008801A1 (en) * | 2002-07-12 | 2004-01-22 | Widex A/S | Hearing aid and a method for enhancing speech intelligibility |
US7242763B2 (en) | 2002-11-26 | 2007-07-10 | Lucent Technologies Inc. | Systems and methods for far-end noise reduction and near-end noise compensation in a mixed time-frequency domain compander to improve signal quality in communications systems |
DE10305369B4 (en) * | 2003-02-10 | 2005-05-19 | Siemens Ag | User-adaptive method for noise modeling |
US7127076B2 (en) | 2003-03-03 | 2006-10-24 | Phonak Ag | Method for manufacturing acoustical devices and for reducing especially wind disturbances |
EP2254352A3 (en) * | 2003-03-03 | 2012-06-13 | Phonak AG | Method for manufacturing acoustical devices and for reducing wind disturbances |
CA2691762C (en) | 2004-08-30 | 2012-04-03 | Qualcomm Incorporated | Method and apparatus for an adaptive de-jitter buffer |
KR100640865B1 (en) | 2004-09-07 | 2006-11-02 | 엘지전자 주식회사 | method and apparatus for enhancing quality of speech |
US8085678B2 (en) | 2004-10-13 | 2011-12-27 | Qualcomm Incorporated | Media (voice) playback (de-jitter) buffer adjustments based on air interface |
US8082156B2 (en) | 2005-01-11 | 2011-12-20 | Nec Corporation | Audio encoding device, audio encoding method, and audio encoding program for encoding a wide-band audio signal |
GB2429139B (en) * | 2005-08-10 | 2010-06-16 | Zarlink Semiconductor Inc | A low complexity noise reduction method |
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
KR100667852B1 (en) * | 2006-01-13 | 2007-01-11 | 삼성전자주식회사 | Apparatus and method for eliminating noise in portable recorder |
EP4178110B1 (en) * | 2006-01-27 | 2024-04-24 | Dolby International AB | Efficient filtering with a complex modulated filterbank |
US9318108B2 (en) | 2010-01-18 | 2016-04-19 | Apple Inc. | Intelligent automated assistant |
KR101414233B1 (en) | 2007-01-05 | 2014-07-02 | 삼성전자 주식회사 | Apparatus and method for improving speech intelligibility |
KR100883896B1 (en) * | 2007-01-19 | 2009-02-17 | 엘지전자 주식회사 | Speech intelligibility enhancement apparatus and method |
KR100876794B1 (en) * | 2007-04-03 | 2009-01-09 | 삼성전자주식회사 | Apparatus and method for enhancing intelligibility of speech in mobile terminal |
US8977255B2 (en) | 2007-04-03 | 2015-03-10 | Apple Inc. | Method and system for operating a multi-function portable electronic device using voice-activation |
EP2191466B1 (en) * | 2007-09-12 | 2013-05-22 | Dolby Laboratories Licensing Corporation | Speech enhancement with voice clarity |
CN101904097B (en) | 2007-12-20 | 2015-05-13 | 艾利森电话股份有限公司 | Noise suppression method and apparatus |
EP2232704A4 (en) * | 2007-12-20 | 2010-12-01 | Ericsson Telefon Ab L M | Noise suppression method and apparatus |
US9330720B2 (en) | 2008-01-03 | 2016-05-03 | Apple Inc. | Methods and apparatus for altering audio output signals |
CN101221767B (en) * | 2008-01-23 | 2012-05-30 | 晨星半导体股份有限公司 | Voice boosting device and method used on the same |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
EP2373067B1 (en) * | 2008-04-18 | 2013-04-17 | Dolby Laboratories Licensing Corporation | Method and apparatus for maintaining speech audibility in multi-channel audio with minimal impact on surround experience |
US10496753B2 (en) | 2010-01-18 | 2019-12-03 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US20100030549A1 (en) | 2008-07-31 | 2010-02-04 | Lee Michael M | Mobile device having human language translation capability with positional feedback |
WO2010067118A1 (en) | 2008-12-11 | 2010-06-17 | Novauris Technologies Limited | Speech recognition involving a mobile device |
DE102009011583A1 (en) | 2009-03-06 | 2010-09-09 | Krones Ag | Method and device for producing and filling thin-walled beverage containers |
US10241644B2 (en) | 2011-06-03 | 2019-03-26 | Apple Inc. | Actionable reminder entries |
US10241752B2 (en) | 2011-09-30 | 2019-03-26 | Apple Inc. | Interface for a virtual digital assistant |
US9858925B2 (en) | 2009-06-05 | 2018-01-02 | Apple Inc. | Using context information to facilitate processing of commands in a virtual assistant |
US10706373B2 (en) | 2011-06-03 | 2020-07-07 | Apple Inc. | Performing actions associated with task items that represent tasks to perform |
US9431006B2 (en) | 2009-07-02 | 2016-08-30 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US10276170B2 (en) | 2010-01-18 | 2019-04-30 | Apple Inc. | Intelligent automated assistant |
US10553209B2 (en) | 2010-01-18 | 2020-02-04 | Apple Inc. | Systems and methods for hands-free notification summaries |
US10705794B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US10679605B2 (en) | 2010-01-18 | 2020-06-09 | Apple Inc. | Hands-free list-reading by intelligent automated assistant |
DE202011111062U1 (en) | 2010-01-25 | 2019-02-19 | Newvaluexchange Ltd. | Device and system for a digital conversation management platform |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
CN102202038B (en) * | 2010-03-24 | 2015-05-06 | 华为技术有限公司 | Method and system for realizing voice energy display, conference server and terminal |
US9837097B2 (en) | 2010-05-24 | 2017-12-05 | Nec Corporation | Single processing method, information processing apparatus and signal processing program |
CN101859569B (en) * | 2010-05-27 | 2012-08-15 | 上海朗谷电子科技有限公司 | Method for lowering noise of digital audio-frequency signal |
US8639516B2 (en) | 2010-06-04 | 2014-01-28 | Apple Inc. | User-specific noise suppression for voice quality improvements |
US10762293B2 (en) | 2010-12-22 | 2020-09-01 | Apple Inc. | Using parts-of-speech tagging and named entity recognition for spelling correction |
CN102128976B (en) * | 2011-01-07 | 2013-05-15 | 钜泉光电科技(上海)股份有限公司 | Energy pulse output method and device of electric energy meter and electric energy meter |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
US10057736B2 (en) | 2011-06-03 | 2018-08-21 | Apple Inc. | Active transport based notifications |
US8994660B2 (en) | 2011-08-29 | 2015-03-31 | Apple Inc. | Text correction processing |
AU2012232977A1 (en) * | 2011-09-30 | 2013-04-18 | Apple Inc. | Using context information to facilitate processing of commands in a virtual assistant |
US10134385B2 (en) | 2012-03-02 | 2018-11-20 | Apple Inc. | Systems and methods for name pronunciation |
US9483461B2 (en) | 2012-03-06 | 2016-11-01 | Apple Inc. | Handling speech synthesis of content for multiple languages |
US9280610B2 (en) | 2012-05-14 | 2016-03-08 | Apple Inc. | Crowd sourcing information to fulfill user requests |
US9721563B2 (en) | 2012-06-08 | 2017-08-01 | Apple Inc. | Name recognition system |
CN102737646A (en) * | 2012-06-21 | 2012-10-17 | 佛山市瀚芯电子科技有限公司 | Real-time dynamic voice noise reduction method for single microphone |
US9495129B2 (en) | 2012-06-29 | 2016-11-15 | Apple Inc. | Device, method, and user interface for voice-activated navigation and browsing of a document |
US9576574B2 (en) | 2012-09-10 | 2017-02-21 | Apple Inc. | Context-sensitive handling of interruptions by intelligent digital assistant |
US9547647B2 (en) | 2012-09-19 | 2017-01-17 | Apple Inc. | Voice-based media searching |
KR20240132105A (en) | 2013-02-07 | 2024-09-02 | 애플 인크. | Voice trigger for a digital assistant |
US10652394B2 (en) | 2013-03-14 | 2020-05-12 | Apple Inc. | System and method for processing voicemail |
US9368114B2 (en) | 2013-03-14 | 2016-06-14 | Apple Inc. | Context-sensitive handling of interruptions |
AU2014233517B2 (en) | 2013-03-15 | 2017-05-25 | Apple Inc. | Training an at least partial voice command system |
WO2014144579A1 (en) | 2013-03-15 | 2014-09-18 | Apple Inc. | System and method for updating an adaptive speech recognition model |
CN104095640A (en) * | 2013-04-03 | 2014-10-15 | 达尔生技股份有限公司 | Oxyhemoglobin saturation detecting method and device |
WO2014197336A1 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for detecting errors in interactions with a voice-based digital assistant |
US9582608B2 (en) | 2013-06-07 | 2017-02-28 | Apple Inc. | Unified ranking with entropy-weighted information for phrase-based semantic auto-completion |
WO2014197334A2 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
WO2014197335A1 (en) | 2013-06-08 | 2014-12-11 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
KR101772152B1 (en) | 2013-06-09 | 2017-08-28 | 애플 인크. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
EP3008964B1 (en) | 2013-06-13 | 2019-09-25 | Apple Inc. | System and method for emergency calls initiated by voice command |
EP2816557B1 (en) * | 2013-06-20 | 2015-11-04 | Harman Becker Automotive Systems GmbH | Identifying spurious signals in audio signals |
US9697831B2 (en) * | 2013-06-26 | 2017-07-04 | Cirrus Logic, Inc. | Speech recognition |
DE112014003653B4 (en) | 2013-08-06 | 2024-04-18 | Apple Inc. | Automatically activate intelligent responses based on activities from remote devices |
US9620105B2 (en) | 2014-05-15 | 2017-04-11 | Apple Inc. | Analyzing audio input for efficient speech and music recognition |
US10592095B2 (en) | 2014-05-23 | 2020-03-17 | Apple Inc. | Instantaneous speaking of content on touch devices |
US9502031B2 (en) | 2014-05-27 | 2016-11-22 | Apple Inc. | Method for supporting dynamic grammars in WFST-based ASR |
US9715875B2 (en) | 2014-05-30 | 2017-07-25 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US9785630B2 (en) | 2014-05-30 | 2017-10-10 | Apple Inc. | Text prediction using combined word N-gram and unigram language models |
US9842101B2 (en) | 2014-05-30 | 2017-12-12 | Apple Inc. | Predictive conversion of language input |
US10170123B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Intelligent assistant for home automation |
US9430463B2 (en) | 2014-05-30 | 2016-08-30 | Apple Inc. | Exemplar-based natural language processing |
US9633004B2 (en) | 2014-05-30 | 2017-04-25 | Apple Inc. | Better resolution when referencing to concepts |
US10078631B2 (en) | 2014-05-30 | 2018-09-18 | Apple Inc. | Entropy-guided text prediction using combined word and character n-gram language models |
US10289433B2 (en) | 2014-05-30 | 2019-05-14 | Apple Inc. | Domain specific language for encoding assistant dialog |
US9734193B2 (en) | 2014-05-30 | 2017-08-15 | Apple Inc. | Determining domain salience ranking from ambiguous words in natural speech |
CN110797019B (en) | 2014-05-30 | 2023-08-29 | 苹果公司 | Multi-command single speech input method |
US9760559B2 (en) | 2014-05-30 | 2017-09-12 | Apple Inc. | Predictive text input |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10659851B2 (en) | 2014-06-30 | 2020-05-19 | Apple Inc. | Real-time digital assistant knowledge updates |
US10446141B2 (en) | 2014-08-28 | 2019-10-15 | Apple Inc. | Automatic speech recognition based on user feedback |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10789041B2 (en) | 2014-09-12 | 2020-09-29 | Apple Inc. | Dynamic thresholds for always listening speech trigger |
US9646609B2 (en) | 2014-09-30 | 2017-05-09 | Apple Inc. | Caching apparatus for serving phonetic pronunciations |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US9886432B2 (en) | 2014-09-30 | 2018-02-06 | Apple Inc. | Parsimonious handling of word inflection via categorical stem + suffix N-gram language models |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US10552013B2 (en) | 2014-12-02 | 2020-02-04 | Apple Inc. | Data detection |
US9711141B2 (en) | 2014-12-09 | 2017-07-18 | Apple Inc. | Disambiguating heteronyms in speech synthesis |
RU2589298C1 (en) * | 2014-12-29 | 2016-07-10 | Александр Юрьевич Бредихин | Method of increasing legible and informative audio signals in the noise situation |
US9865280B2 (en) | 2015-03-06 | 2018-01-09 | Apple Inc. | Structured dictation using intelligent automated assistants |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US9899019B2 (en) | 2015-03-18 | 2018-02-20 | Apple Inc. | Systems and methods for structured stem and suffix language models |
US9842105B2 (en) | 2015-04-16 | 2017-12-12 | Apple Inc. | Parsimonious continuous-space phrase representations for natural language processing |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
US10127220B2 (en) | 2015-06-04 | 2018-11-13 | Apple Inc. | Language identification from short strings |
US10101822B2 (en) | 2015-06-05 | 2018-10-16 | Apple Inc. | Language input correction |
US10255907B2 (en) | 2015-06-07 | 2019-04-09 | Apple Inc. | Automatic accent detection using acoustic models |
US10186254B2 (en) | 2015-06-07 | 2019-01-22 | Apple Inc. | Context-based endpoint detection |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US9697820B2 (en) | 2015-09-24 | 2017-07-04 | Apple Inc. | Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US11587559B2 (en) | 2015-09-30 | 2023-02-21 | Apple Inc. | Intelligent device identification |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
EP3374990B1 (en) | 2015-11-09 | 2019-09-04 | Nextlink IPR AB | Method of and system for noise suppression |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
CN105869650B (en) * | 2015-12-28 | 2020-03-06 | 乐融致新电子科技(天津)有限公司 | Digital audio data playing method and device |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
CN106060717A (en) * | 2016-05-26 | 2016-10-26 | 广东睿盟计算机科技有限公司 | High-definition dynamic noise-reduction pickup |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
DK179588B1 (en) | 2016-06-09 | 2019-02-22 | Apple Inc. | Intelligent automated assistant in a home environment |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10586535B2 (en) | 2016-06-10 | 2020-03-10 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
DK201670540A1 (en) | 2016-06-11 | 2018-01-08 | Apple Inc | Application integration with a digital assistant |
DK179049B1 (en) | 2016-06-11 | 2017-09-18 | Apple Inc | Data driven natural language event detection and classification |
DK179415B1 (en) | 2016-06-11 | 2018-06-14 | Apple Inc | Intelligent device arbitration and control |
DK179343B1 (en) | 2016-06-11 | 2018-05-14 | Apple Inc | Intelligent task discovery |
US9748929B1 (en) * | 2016-10-24 | 2017-08-29 | Analog Devices, Inc. | Envelope-dependent order-varying filter control |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
CN107039044B (en) * | 2017-03-08 | 2020-04-21 | Oppo广东移动通信有限公司 | Voice signal processing method and mobile terminal |
DK179745B1 (en) | 2017-05-12 | 2019-05-01 | Apple Inc. | SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT |
DK201770431A1 (en) | 2017-05-15 | 2018-12-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
US10157627B1 (en) | 2017-06-02 | 2018-12-18 | Bose Corporation | Dynamic spectral filtering |
WO2019187841A1 (en) * | 2018-03-30 | 2019-10-03 | パナソニックIpマネジメント株式会社 | Noise reduction device |
RU2680735C1 (en) * | 2018-10-15 | 2019-02-26 | Акционерное общество "Концерн "Созвездие" | Method of separation of speech and pauses by analysis of the values of phases of frequency components of noise and signal |
CN109643554B (en) * | 2018-11-28 | 2023-07-21 | 深圳市汇顶科技股份有限公司 | Adaptive voice enhancement method and electronic equipment |
US11438452B1 (en) | 2019-08-09 | 2022-09-06 | Apple Inc. | Propagating context information in a privacy preserving manner |
CN112581935B (en) | 2019-09-27 | 2024-09-06 | 苹果公司 | Context-aware speech assistance devices and related systems and methods |
US11501758B2 (en) | 2019-09-27 | 2022-11-15 | Apple Inc. | Environment aware voice-assistant devices, and related systems and methods |
CN111370033B (en) * | 2020-03-13 | 2023-09-22 | 北京字节跳动网络技术有限公司 | Keyboard sound processing method and device, terminal equipment and storage medium |
US20230305590A1 (en) * | 2020-03-13 | 2023-09-28 | University Of South Australia | A data processing method |
CN111402916B (en) * | 2020-03-24 | 2023-08-04 | 青岛罗博智慧教育技术有限公司 | Voice enhancement system, method and handwriting board |
CN114093391A (en) * | 2020-07-29 | 2022-02-25 | 华为技术有限公司 | Abnormal signal filtering method and device |
CN111916106B (en) * | 2020-08-17 | 2021-06-15 | 牡丹江医学院 | Method for improving pronunciation quality in English teaching |
CN112927715B (en) * | 2021-02-26 | 2024-06-14 | 腾讯音乐娱乐科技(深圳)有限公司 | Audio processing method, equipment and computer readable storage medium |
CN114550740B (en) * | 2022-04-26 | 2022-07-15 | 天津市北海通信技术有限公司 | Voice definition algorithm under noise and train audio playing method and system thereof |
CN118411998B (en) * | 2024-07-02 | 2024-09-24 | 杭州知聊信息技术有限公司 | Audio noise processing method and system based on big data |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0645756A1 (en) * | 1993-09-29 | 1995-03-29 | Ericsson Ge Mobile Communications Inc. | System for adaptively reducing noise in speech signals |
Family Cites Families (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4461025A (en) * | 1982-06-22 | 1984-07-17 | Audiological Engineering Corporation | Automatic background noise suppressor |
US4630305A (en) * | 1985-07-01 | 1986-12-16 | Motorola, Inc. | Automatic gain selector for a noise suppression system |
US4811404A (en) * | 1987-10-01 | 1989-03-07 | Motorola, Inc. | Noise suppression system |
DE4012349A1 (en) * | 1989-04-19 | 1990-10-25 | Ricoh Kk | Noise elimination device for speech recognition system - uses spectral subtraction of sampled noise values from sampled speech values |
JP3065739B2 (en) * | 1991-10-14 | 2000-07-17 | 三菱電機株式会社 | Voice section detection device |
US5412735A (en) * | 1992-02-27 | 1995-05-02 | Central Institute For The Deaf | Adaptive noise reduction circuit for a sound reproduction system |
JPH05259928A (en) * | 1992-03-09 | 1993-10-08 | Oki Electric Ind Co Ltd | Method and device for canceling adaptive control noise |
US5251263A (en) * | 1992-05-22 | 1993-10-05 | Andrea Electronics Corporation | Adaptive noise cancellation and speech enhancement system and apparatus therefor |
JPH0695693A (en) * | 1992-09-09 | 1994-04-08 | Fujitsu Ten Ltd | Noise reducing circuit for voice recognition device |
JP3270866B2 (en) * | 1993-03-23 | 2002-04-02 | ソニー株式会社 | Noise removal method and noise removal device |
US5657422A (en) * | 1994-01-28 | 1997-08-12 | Lucent Technologies Inc. | Voice activity detection driven noise remediator |
-
1996
- 1996-09-13 PL PL96325532A patent/PL185513B1/en not_active IP Right Cessation
- 1996-09-13 RU RU98107313/09A patent/RU2163032C2/en not_active IP Right Cessation
- 1996-09-13 BR BR9610290A patent/BR9610290A/en not_active IP Right Cessation
- 1996-09-13 CA CA002231107A patent/CA2231107A1/en not_active Abandoned
- 1996-09-13 JP JP9512112A patent/JPH11514453A/en not_active Ceased
- 1996-09-13 DE DE69613380T patent/DE69613380D1/en not_active Expired - Lifetime
- 1996-09-13 TR TR1998/00475T patent/TR199800475T1/en unknown
- 1996-09-13 KR KR10-1998-0701913A patent/KR100423029B1/en not_active IP Right Cessation
- 1996-09-13 CN CN96198008A patent/CN1121684C/en not_active Expired - Fee Related
- 1996-09-13 WO PCT/US1996/014665 patent/WO1997010586A1/en active IP Right Grant
- 1996-09-13 EE EE9800068A patent/EE03456B1/en not_active IP Right Cessation
- 1996-09-13 AU AU70784/96A patent/AU724111B2/en not_active Ceased
- 1996-09-13 EP EP96931552A patent/EP0852052B1/en not_active Expired - Lifetime
-
1998
- 1998-03-09 MX MX9801857A patent/MX9801857A/en unknown
- 1998-03-11 NO NO981074A patent/NO981074L/en not_active Application Discontinuation
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0645756A1 (en) * | 1993-09-29 | 1995-03-29 | Ericsson Ge Mobile Communications Inc. | System for adaptively reducing noise in speech signals |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10249317B2 (en) | 2014-07-28 | 2019-04-02 | Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. | Estimating noise of an audio signal in a LOG2-domain |
US10762912B2 (en) | 2014-07-28 | 2020-09-01 | Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. | Estimating noise in an audio signal in the LOG2-domain |
US11335355B2 (en) | 2014-07-28 | 2022-05-17 | Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. | Estimating noise of an audio signal in the log2-domain |
Also Published As
Publication number | Publication date |
---|---|
PL185513B1 (en) | 2003-05-30 |
KR19990044659A (en) | 1999-06-25 |
CN1201547A (en) | 1998-12-09 |
PL325532A1 (en) | 1998-08-03 |
EE9800068A (en) | 1998-08-17 |
AU7078496A (en) | 1997-04-01 |
NO981074L (en) | 1998-05-13 |
KR100423029B1 (en) | 2004-07-01 |
WO1997010586A1 (en) | 1997-03-20 |
EE03456B1 (en) | 2001-06-15 |
NO981074D0 (en) | 1998-03-11 |
CA2231107A1 (en) | 1997-03-20 |
CN1121684C (en) | 2003-09-17 |
BR9610290A (en) | 1999-03-16 |
RU2163032C2 (en) | 2001-02-10 |
DE69613380D1 (en) | 2001-07-19 |
TR199800475T1 (en) | 1998-06-22 |
EP0852052A1 (en) | 1998-07-08 |
EP0852052B1 (en) | 2001-06-13 |
JPH11514453A (en) | 1999-12-07 |
MX9801857A (en) | 1998-11-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
AU724111B2 (en) | System for adaptively filtering audio signals to enhance speech intelligibility in noisy environmental conditions | |
CA2117587C (en) | System for adaptively reducing noise in speech signals | |
EP1017042B1 (en) | Voice activity detection driven noise remediator | |
US8019599B2 (en) | Speech codecs | |
AU763409B2 (en) | Complex signal activity detection for improved speech/noise classification of an audio signal | |
US5544250A (en) | Noise suppression system and method therefor | |
JP2002501225A (en) | Decoding method and system with adaptive postfilter | |
WO1997022117A1 (en) | Method and device for voice activity detection and a communication device | |
KR19990007936A (en) | Battery-powered radio transceiver with improved battery life and how to operate it | |
JP2003524796A (en) | Method and apparatus for crossing line spectral information quantization method in speech coder | |
US5710862A (en) | Method and apparatus for reducing an undesirable characteristic of a spectral estimate of a noise signal between occurrences of voice signals | |
EP1242992A2 (en) | A noise suppressor | |
EP1040467A1 (en) | Communication terminal | |
JP2002076960A (en) | Noise suppressing method and mobile telephone | |
WO2001022401A1 (en) | Processing circuit for correcting audio signals, receiver, communication system, mobile apparatus and related method | |
WO2001041334A1 (en) | Method and apparatus for suppressing acoustic background noise in a communication system |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
FGA | Letters patent sealed or granted (standard patent) |