EP2980800A1 - Geräuschpegelschätzung - Google Patents

Geräuschpegelschätzung Download PDF

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EP2980800A1
EP2980800A1 EP14179096.4A EP14179096A EP2980800A1 EP 2980800 A1 EP2980800 A1 EP 2980800A1 EP 14179096 A EP14179096 A EP 14179096A EP 2980800 A1 EP2980800 A1 EP 2980800A1
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Prior art keywords
noise
signal
probability
level
noise signal
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French (fr)
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Dolby Laboratories Licensing Corp
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Dolby Laboratories Licensing Corp
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Priority to EP14179096.4A priority Critical patent/EP2980800A1/de
Priority to PCT/US2015/034733 priority patent/WO2015191470A1/en
Priority to EP15729062.8A priority patent/EP3152756B1/de
Priority to US15/316,092 priority patent/US10141003B2/en
Publication of EP2980800A1 publication Critical patent/EP2980800A1/de
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    • 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
    • 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
    • G10L21/0264—Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques
    • 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
    • G10L21/0316—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude
    • G10L21/0324—Details of processing therefor
    • G10L21/034—Automatic adjustment
    • 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
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters

Definitions

  • Embodiments of the present invention generally relate to audio processing, and more specifically, to method and system for noise level estimation.
  • Real-life noise may consist of different types of noises: stationary and non-stationary noises.
  • the non-stationary noise may include two classes: an abrupt increase of noise floor and an impulsive noise, which are particularly challenging for audio processing with the noise signal concerned.
  • the abrupt increase of noise floor refers to the noise floor suddenly increasing from one level to another level and maintaining substantially stationary within a relative long period of time
  • the impulsive noise refers to a non-stationary noise the level of which increases suddenly and then drops down within a short period of time.
  • the noise level is required to be tracked.
  • the estimated level of a noise signal may directly impact the gain applied to the audio signal.
  • noise signal input should be processed to obtain an estimated noise level that can be used in audio processing, as illustrated in Figure 1 .
  • Noise estimation techniques have been developed mainly in the framework of speech processing, especially in speech enhancement. These techniques, for example, may be divided into: minimum tracking, time-recursive averaging, histogram based noise estimation, and quantile based noise estimation and so on. Concerning the two classes of non-stationary noises, the estimated noise level is desired to follow the abrupt increase of noise floor and to resist a drift of noise estimation during the short-period impulsive noise. However, the existing noise estimation methods are either too sensitive to the abrupt increase of noise floor or too sensitive to the impulsive noise, disabling to estimate a robust level of noise in these two noise scenarios.
  • embodiments of the present invention proposes a method and system for noise level estimation.
  • embodiments of the present invention provide a method for noise level estimation.
  • the method comprises: responsive to an increase of a signal level of a noise signal, calculating an impulsive noise probability of the noise signal, the impulsive noise probability indicating a likelihood that the noise signal is an impulsive noise; determining a variable smoothing factor for noise level estimation based on the impulsive noise probability, the variable smoothing factor being associated with a previous estimated level of the noise signal; and smoothing the noise signal with the variable smoothing factor so as to determine a current estimated level of the noise signal.
  • Embodiments in this regard further comprise a corresponding computer program product.
  • inventions of the present invention provide a system for noise level estimation.
  • the system comprises: an impulsive noise probability calculation unit, configured to calculate an impulsive noise probability of a noise signal responsive to an increase of a signal level of the noise signal, the impulsive noise probability indicating a likelihood that the noise signal is an impulsive noise; a smoothing factor determination unit, configured to determine a variable smoothing factor for noise level estimation based on the impulsive noise probability, the variable smoothing factor being associated with a previous estimated level of the noise signal; and a noise level estimation unit, configured to smooth the noise signal with the variable smoothing factor so as to determine a current estimated level of the noise signal.
  • the estimated noise level can be resist from drifting in the scenario of impulsive noise based on the variable smoothing factor that is determined from the impulsive noise probability.
  • an abrupt increase of noise floor and an impulsive noise are particularly challenging for audio processing with the noise signal concerned, and in noise estimation, the estimated noise level is desired to follow the abrupt increase of noise floor and to resist a drift of noise estimation during the short-period impulsive noise floor.
  • the increase of noise floor and the impulsive noise should be distinguished from the noise signal input, and then different smoothing factors can be applied to smooth the noise signal input.
  • An easy solution to distinguish is to look-ahead and buffer enough length of signal to build up sufficient confidence on the signal type and later process it accordingly, which, however, may cause large latency.
  • Embodiments of the present invention propose a method and system for robust noise level estimation, which can track the noise level closely and smoothly, follow the increase of noise floor fast and resist the short-period impulsive noise.
  • the method and system of the present invention introduce impulsive noise probability and adaptive smoothing factors so as to achieve low-latency and accurate classification of signal types and robust noise level estimation.
  • Figure 2 shows a flowchart of a method 200 for noise level estimation in accordance with example embodiments of the present invention.
  • an impulsive noise probability of the noise signal is calculated.
  • the impulsive noise probability indicates a likelihood that the noise signal is an impulsive noise.
  • the noise signal input may be, for example, obtained from microphone input or processed microphone signals, and may be any daily-life stationary or non-stationary noise.
  • the noise level estimation may be performed band by band, or on a broadband.
  • the noise level estimation at different frequency bands may be performed concurrently or in sequence.
  • the noise signal to be estimated may be a signal in one of a plurality of frequency bands of the noise input signal, or is a broadband signal of the noise input signal.
  • One purpose of the invention is to quickly and accurately distinguish the increase of noise floor and the impulsive noise, both of which have an increasing signal level at early stage.
  • the increasing signal level may be used to trigger the calculation of the impulsive noise probability.
  • an onset detector may be used to track the onset of the noise signal, and the probability of signal level increasing may be represented as an onset probability.
  • an onset probability of the noise signal may be determined, and the onset probability indicates the likelihood of the increase of the signal level. The determination of onset probability will be discussed with more details below with reference to Figure 3 .
  • impulsive noise probability indicating the likelihood of the noise signal being an impulsive noise.
  • the impulsive noise probability is between 0 and 1.
  • the probability of noise floor increase may also be determined by 1 minus the impulsive noise probability.
  • the abrupt increase of noise floor refers to the noise floor suddenly increasing from one level to another level and maintaining substantially stationary within a relative long period of time
  • the impulsive noise refers to a non-stationary noise the level of which increases suddenly and then drops down within a short period of time.
  • a person in a corner shop is exposed to a relatively quiet indoor environment. When he opens the door, he suddenly enters an environment of high-level ambient traffic noise and may hear a loud door slam afterwards on top of the traffic noise.
  • the traffic noise may result in an increase of noise floor, and the noise of door slam, the level of which drops down quickly, may be classified into the impulsive noise.
  • step S202 a variable smoothing factor is determined for noise level estimation based on the impulsive noise probability at step S201.
  • variable smoothing factor is associated with a previous estimated level of the noise signal.
  • the variable smoothing factor is used to smooth the noise signal so as to have the estimated level of the noise signal following the increase of noise floor fast and resisting the short-period impulsive noise. Since the smaller smoothing factor may result in the estimated noise level following the actual level of the noise signal more quickly and versa vice, if the calculated impulsive noise probability is smaller, which means the noise signal is probably an increase of noise floor, then the variable smoothing factor is determined to be smaller, and versa vice.
  • the noise signal input may also be smoothed with a constant smoothing factor, which is referred to as a reference smoothing factor herein, so as to produce a smoothed noise signal with more smoothed level for later processing. Therefore, the determined variable smoothing factor at step S202 may be between this constant smoothing factor and 1. The determination of variable smoothing factor will be discussed with more details below with reference to Figure 3 .
  • step S203 the noise signal is smoothed with the variable smoothing factor so as to determine a current estimated level of the noise signal.
  • This estimated level may be used in later audio processing, such as noise compensation, speech enhancement or the like. This step will also be discussed with more details below with reference to Figure 3 .
  • Figure 3 illustrates a schematic diagram 300 of noise level estimation in accordance with one example embodiment of the present invention.
  • the processing of noise level estimation will be described more detailed.
  • one or more of the blocks shown in Figure 3 may be optional and thus can be omitted in some embodiments, and some of the blocks may be combined as one block or one block may be divided into multiple blocks in practice. The scope of the present invention is not limited in this regard.
  • the processing of noise level estimation may be performed for each of a plurality of frequency bands, and the parameters may be tuned band by band.
  • the noise signal to be estimated may be a signal in one of a plurality of frequency bands of a noise input signal, or is a broadband signal of the noise input signal.
  • the frequency of a noise signal input may be divided into five bands, B1 ( f ⁇ 700Hz), B2 (700Hz ⁇ f ⁇ 1000Hz), B3 (1000Hz ⁇ f ⁇ 2000Hz), B4 (2000Hz ⁇ f ⁇ 6000Hz), and B5 ( f >6000Hz), and the noise signal to be estimated may be at one of the five bands.
  • the output of block 301 may be X ( f , t ), wherein X ( f,t ) may represent the actual signal level of a noise signal in one frequency band and at a point of time in one example embodiment.
  • the block 301 may be optional in other embodiments if a broadband noise signal is used in the processing.
  • the noise signal may be represented as X ( t ) for example.
  • this block may also determine the processing interval ⁇ t of the noise level estimation, that is, it may output the noise signal X ( f,t ) every other processing interval ⁇ t .
  • the processing interval ⁇ t may be determined by the sample rate.
  • this block may output the noise signal X ( f, t ) every other sample, or every other ten samples, or the like.
  • Two smoothers may be used to smooth the noise signal before it is input into the blocks of onset probability, maximum tracking or impulsive noise probability. As the actual level of the noise signal changes too frequently, pre-smoothing operations may produce more smoothed noise signal for later processing.
  • the block of fast smoothing 302 may utilize a small smoothing factor to smooth the noise signal so as to track the actual noise level quickly.
  • the block of slow smoothing 303 may utilize a large smoothing factor to smooth the noise signal so as to track the actual noise level slowly.
  • the onset probability of the noise signal indicates a likelihood of the increase of the signal level, and may be used to trigger the calculation of impulsive noise probability.
  • the input of the onset probability block 304 is the slow smoothed noise signal X sm 2 ( f,t ) , as illustrated in Figure 3 . It should be noted that, in other embodiments, this block may have the fast smoothed noise signal X sm 1 ( f,t ) or the raw noise signal X ( f, t ) as the input.
  • the onset probability is determined based on the crest factor, which is the peak to root-mean-square (rms) ratio. In one example, if the crest factor is larger than a threshold, the onset probability may be determined as 1, and otherwise may be determined as 0. In other examples, the onset probability may be measured as a continuous value between 0 and 1, and may be smoothed from the previous onset probability in order to avoid a sudden change.
  • X sm 2 ,dB ( f,t ) represents the output X sm 2 ( f,t ) of the slow smoothing block 303 in log domain
  • X sm 3 ,dB ( f,t ) represents a further smoothed noise signal in log domain approximating a rms estimation
  • a g represents the smoothing factor for X sm 3 ,dB ( f,t) and is between 0 and 1, which, in one example, may
  • onset probability may be determined as 1.
  • the scope of the present invention is not limited in this regard.
  • the onset probability is input to the block of impulsive noise probability 306 and is used to trigger the calculation of the impulsive noise probability.
  • the calculation of the impulsive noise probability may comprise the following steps: (1) setting an initial value of the impulsive noise probability as the onset probability, when the onset probability is higher than a first predetermined probability threshold; (2) determining whether the noise signal has a decay trend; and (3) calculating the impulsive noise probability of the noise signal based on whether the noise signal has the decay trend.
  • the calculation of impulsive noise probability may be triggered by the onset probability. For example, if the onset probability is higher than 0.5, the calculation of impulsive noise probability begins and the initial value of the impulsive noise probability may be set as the onset probability. It should be noted that the first predetermined probability threshold may be other values between 0 and 1, and the initial value of the impulsive noise probability may be set as other values, such as a value lower or higher than the onset probability, or a fixed value.
  • an impulse status indicator may be used to indicate whether the noise signal is currently estimated as an impulsive noise or not.
  • this impulse status indicator may be a Boolean variable. That is, if the noise signal is currently estimated as an impulsive noise, the impulse status indicator may be represented as True, and otherwise, it may be represented as False. It should be noted that the impulse status indicator may have other values. For example, it may have a value of 0 or 1 to indicate the status of impulse.
  • the impulse status indicator before beginning to calculate the impulsive noise probability, the impulse status indicator may first be determined as False. Then, when the onset probability is higher than the first predetermined probability threshold, the calculation of impulsive noise probability may begin and the impulse status indicator may be set as True.
  • the calculation of impulsive noise probability is based on the decay trend, as the impulsive noise has a decay nature within a short period of time, which may not be present in the increase of noise floor.
  • the noise includes an abrupt level changed from 0 dB to around 30 dB at around 1.8 seconds and lasts for more than 5 second.
  • This noise also includes a door slam at around 7.6 seconds, which has an onset of 40 dB and lasts for only 1.6 seconds.
  • the door slam part contains a lot of variations in the magnitude over time, the general level is decaying.
  • a maximal signal level of the noise signal within a first time window may be determined first, and then whether the noise signal has the decay trend may be determined based on a distance between the maximal signal level and the signal level of the noise signal.
  • the block of maximum tracking 305 is used to track the maximal signal level within the first time window.
  • the block of maximum tracking 305 may begin to operate when the block of impulsive noise probability 306 begins calculating.
  • the maximum tracking may also be triggered by the onset probability in some embodiments, and may also be triggered by whether the impulse status indicator is False in some other embodiments.
  • the original value of the maximal signal level may be set as the signal level of the noise signal in some example embodiments of the present invention.
  • the original value of the maximal signal level may be set as the raw signal level X ( f , t ), the slow smoothed signal level X sm 2 ( f,t )or the fast smoothed signal level X sm 1 ( f,t ) .
  • the length of the first time window may be predetermined, such as 2 seconds or 3 seconds.
  • the signal level of impulsive noise may be the output of the fast smoothing block 302 as illustrated in Figure 3 , and may also be the raw signal level or the output of the slow smoothing block 303.
  • the decay trend in the noise signal may be detected.
  • a threshold may be used to measure if the distance is such large that a decay trend is probably contained in this noise.
  • whether the noise signal has a decay trend is determined based on a slope of the noise signal over time. For example, a slope of the magnitudes of the noise signal at two points of time may be calculated, and if this slope is negative, it means that the noise level is decaying between these two points of time. In other examples, in order to improve confidence, multiple slopes may be determined. By analyzing the slopes, it may be determined whether there is a decay trend in the noise signal.
  • the noise signal when calculating the impulsive noise probability, if the noise signal is determined to have a decay trend, it means that the noise signal may probably be an impulsive noise, and versa visa. As such, the impulsive noise probability is increased at a first rate when the noise signal has the decay trend, and the impulsive noise probability is decreased at a second rate when the noise signal has no decay trend.
  • the distance is larger than D 0 , it may be regarded as a positive sign of impulsive noise, and otherwise a negative sign.
  • P imp ( f,t ) is going down as the noise signal does not look like an impulse, and if there is a decay trend, P imp ( f,t ) is going up.
  • the calculation of impulsive noise probability may be performed band by band. Considering the decay trends of the noise signal may occurs at different time for different frequency bands, if the noise signals in one or more frequency band are determined to have decay trend, it means that the other frequency bands may also decay sooner or later. As such, if the calculated impulsive noise probability for at least one frequency band of the noise input signal is higher than a confidence threshold, the impulsive noise probabilities for the remaining frequency bands are increased. Then, accurate decision for the distinguishment of the impulsive noise and noise floor increase may be accelerated at all frequency bands.
  • the impulsive noise probability of noise signal in B1 ( f ⁇ 700Hz) is determined first to be 0.6, which is higher than the confidence threshold 0.5, then the impulsive noise probabilities of noise signals in other frequency bands may be increased to 0.6.
  • impulsive noise probabilities for the remaining frequency bands may not necessarily be the same, and in some example embodiments, not all impulsive noise probabilities for the remaining frequency bands are increased.
  • the scope of the present invention is not limited in this regard.
  • the onset probability may be smaller at the ending of an impulse, and then the impulsive noise probability may also decrease at this point of time.
  • Another sign of the ending of the impulse is that a distance between the current estimated level of the noise signal and the signal level of the noise signal is lower than an error tolerance, because the estimated level is designed to resist the impulsive noise.
  • the signal level of the noise signal used here may be the raw noise level, or may be the fast or slow smoothed noise level output from the block 302 or 303.
  • the block of maximum tracking 305 may keep tracking the maximum of noise level until the onset probability is lower than a second predetermined probability threshold and a distance between the current estimated level of the noise signal and the signal level of the noise signal is lower than a predetermined distance threshold.
  • the maximal signal level is decreased in further embodiments of the present invention.
  • the maximal signal level may be decreased to the raw noise level, the fast or slow smoothed noise level, or the estimated noise level at the time of the above conditions are met.
  • the conditions in Equation (10) generally indicate the ending of an impulse as the signal level of the noise signal X sm 2 ,dB ( f,t ) may go down to the estimated level Y ( f,t ). In this case, the maximal signal level may also be set to Y ( f,t ) .
  • an impulse status indicator may be used to indicate whether the noise signal is currently estimated as an impulsive noise. Since the conditions in Equation (10) indicate the ending of an impulse, the impulse status indicator may also be changed to False, which may then stop the maximum tracking in some embodiments.
  • Equation (8) may also be used.
  • the condition D dB ( f,t ) > D 0 in Equation (8) may generally be covered by that "the noise signal has a decay trend.”
  • the impulsive noise may be calculated in other ways, as long as the impulsive noise probability increases if a decay trend is detected and decreases if no decay trend is detected.
  • an impulse establishment time is recorded when beginning to calculate the impulsive noise probability.
  • the impulse establishment time may be recorded by the block 306 in Figure 3 in an example.
  • the impulse establishment time is set to zero when the onset probability is higher than the first predetermined threshold.
  • the calculation of impulsive noise probability may be triggered, which means that a new onset is detected.
  • the impulse establishment time may be set to zero and the time the new detected impulse has last may be recorded.
  • the impulse establishment time T imp ( f,t ) is output to the block of adaptive smoothing 307 to facilitate the operations of this block.
  • the block of adaptive smoothing 307 in Figure 3 may be used to perform the determination of the variable smoothing factor at step S202 of the method 200 and the smoothing based on the variable smoothing factor at step S203 of the method 200.
  • the block 307 may have the input of the noise signal from the block 301, the onset probability from the block 304, and the impulsive noise probability and the impulse establishment time from the block 306.
  • variable smoothing factor is determined based on the impulsive noise probability, and in some embodiments, the impulse establishment time may be used to avoid disturbing by a series of onsets. More particularly, the variable smoothing factor is determined based on a reference smoothing factor and a maximum of the impulsive noise probability and the onset probability, when the impulse establishment time is lower than a predetermined time threshold; and the variable smoothing factor is determined based on the reference smoothing factor and the impulsive noise probability, when the impulse establishment time is higher than or equal to the predetermined time threshold. In determination, the variable smoothing factor may be a decreasing function of the impulsive noise probability over time.
  • Equation (12) it would be understood from Equation (12) that when onset is just detected, the impulsive noise probability has not been established, in order to avoid following up other onsets, max[ P imp ( f , t ), P on ( f , t )] is used to calculate ⁇ ( f , t ). After T 0 , the impulsive noise probability has been established, and only P imp ( f,t ) is used.
  • the estimated level of the noise signal is determined based on the variable smoothing factor.
  • the noise signal is smoothed with the variable smoothing factor; and a smoothed signal level of the smoothed noise signal is determined as the current estimated level of the noise signal.
  • Y dB ( f,t ) represents the current estimated level in log domain, which is equal to the smoothed signal level
  • Y dB ( f,t- ⁇ t ) represent the previous estimated level in log domain
  • X dB ( f, t ) presents the raw signal level in log domain.
  • the variable smoothing factor determined is associated with the previous estimated level and used to smooth the noise signal.
  • Equation (12) the variable smoothing factor increases and decreases as the impulsive noise probability increases and decreases.
  • Equation (13) it can be seen that, the larger the variable smoothing factor is, the slower the changing of the estimated level over time is.
  • the impulsive noise probability is determined to be large, which means that the noise signal may probably be an impulse
  • the estimated level may resist the noise signal, which indicates a slow reacting estimation.
  • the impulsive noise probability is equal to 1, according to Equations (12) and (13), the estimated level will hold at the previous estimated level and will not follow the increase of impulse level. As such, it appears that the impulsive noise will be ignored in the later audio processing.
  • the noise level estimation allows following the noise floor increase and resisting the short-period impulsive noise.
  • the noise floor increase as can be seen from Equation (12) for example, if the impulsive noise probability indicates that the noise signal is probably an increase of noise floor, the adaptive smoothing factor will equal or near to the reference smoothing factor, which is used for smoothing the noise signal without abrupt increase and may usually small. Then when determining the estimated level by this adaptive smoothing factor, the estimated level may increase slowly from the level before the onset to the later stationary level of the noise floor. This latency is desired to be reduced.
  • Figure 4 illustrates a schematic diagram of noise level estimation in accordance with another example embodiment of the present invention. For purposes of illustration, certain references with respect to Figure 3 are maintained the same. In Figure 4 , additional blocks are added to reduce the latency of noise level estimation in the case of noise floor increase.
  • the block of minimum tracking 309 may be used to track a minimal signal level of the noise signal within a second time window.
  • the estimated noise level may be directly set to this minimal signal level as it is larger than the level smoothed with the low variable smoothing factor as discussed in the above.
  • the current estimated noise level may be brought up if the minimal signal level X min , dB ( f,t ) is determined as the lowest level at the stage the noise floor level has increased and remains stationary.
  • the length of the second time window may be predetermined, for example, as 2 seconds, and the scope of the present invention is not limited in this regard.
  • the length of the second time window may impact the time the minimal signal level taken to be determined as the lowest level at the stationary stage of the noise floor.
  • the lowest level at the stationary stage may be tracked quickly if a smaller time window is used.
  • the second time window may be adaptively narrowed down as the noise floor is becoming stable.
  • a degree of stability of the noise signal may be determined; and the second time window may be narrowed down when the degree of stability is lower than a predetermined stability threshold and the impulse establishment time is lower than the predetermined time threshold, such that the minimal signal level of the noise signal within the narrowed second time window is larger than the smoothed signal level.
  • the degree of stability may be used to measure how stable the noise signal is, as the noise floor may trend to be stationary at last. Moreover, only the degree of stability may not be enough to decide that the second time window should be narrowed down as the impulsive noise may also go down to a stable level. Therefore, the impulse establishment time T imp ( f,t ) may be used to limit that if the noise signal becomes stationary at early stage, there is a tendency that the onset may be actually caused by a noise floor increase. Under this condition, the second time window is desired to be narrowed down so as to bring the estimated noise level instantly as shown in Equation (14).
  • the degree of stability is measured by a variance or standard deviation of the noise signal within a predetermined measurement time window. Additionally or alternatively, many other parameters may be used to measure the degree of stability, for example, the probability of the noise floor increase, which may be equal to 1 minus the impulsive noise probability. The scope of the present invention is not limited in this regard.
  • the shorter second time window may always be used by the block of minimum tracking 309 to track the minimal signal level. That is, the second time window may not necessarily to be changed but remain short enough so as to enable the estimated level being brought up quickly when needed.
  • the standard deviation of the noise signal may be used to measure the degree of stability of the noise signal, and this block 308 may be used to calculate the standard deviation. It should be noted that if other parameters are additionally or alternatively required to measure the degree of stability, this block 308 may be replaced or other blocks may be added.
  • Equation (16) calculates the standard deviation of the slow smoothed noise signal X sm 2 ( f,t ) within a time window of m ⁇ t, and in other examples, the standard deviation of the fast smoothed noise signal X sm 1 ( f,t )
  • Equation (17) is used to smooth the standard deviation of Equation (16) in log domain, and ⁇ S is the smoothing factor that is between 0 and 1.
  • Equation (18) normalizes the smoothed standard deviation so that Std ( f,t ) is all above zero and is irrelevant with the magnitude of X sm 2 ( f , t ), which makes it easier to set a threshold for judging how stationary the noise signal is.
  • Std ( f,t ) is the standard deviation of "smoothed standard deviation of X sm 2 ( f,t ) in log domain within a time window of n ⁇ t.”
  • the value of m ⁇ t may be 0.5 second and the value of n ⁇ t may be 0.8 seconds. The scope of the present invention is not limited in this regard.
  • Figure 5(a) shows the comparison of the estimated noise level in accordance with an existing method and an example embodiment of the present invention.
  • Figure 5(b)-(d) illustrates graphs of the parameters used in the example embodiment of Figure 5(a) .
  • the noise signal includes an abrupt level changed from 0 dB to around 30 dB at around 1.8 seconds, a door slam at around 7.6 seconds, and four quick knocks in series on the door from 16.1 seconds to 17.1 seconds.
  • the door slam and four knocks may be regarded as impulsive noises.
  • Figure 5(a) it shows the estimated noise level tracked by an existing method. The idea of this method is that when onset is detected, no matter the onset is caused by an increase of noise floor or an impulsive noise (these two types of noise signal cannot be distinguished in the existing method), the estimated noise level will track the actual signal level of the noise signal.
  • Figure 6 shows a block diagram of a system 600 for noise level estimation in accordance with one example embodiment of the present invention is shown.
  • the system 600 comprises an impulsive noise probability calculation unit 601 configured to calculate an impulsive noise probability of a noise signal responsive to an increase of a signal level of the noise signal, the impulsive noise probability indicating a likelihood that the noise signal is an impulsive noise.
  • the system 600 also comprises a smoothing factor determination unit 602 configured to determine a variable smoothing factor for noise level estimation based on the impulsive noise probability, the variable smoothing factor being associated with a previous estimated level of the noise signal.
  • the system 600 further comprises a noise level estimation unit 603 configured to smooth the noise signal with the variable smoothing factor so as to determine a current estimated level of the noise signal.
  • the noise signal may be a signal in one of a plurality of frequency bands of a noise input signal, or is a broadband signal of the noise input signal. In these embodiments, if the calculated impulsive noise probability for at least one frequency band of the noise input signal is higher than a confidence threshold, the impulsive noise probabilities for the remaining frequency bands may be increased.
  • the system 600 may further comprise an onset probability determination unit, configured to determine an onset probability of the noise signal, the onset probability indicating a likelihood of the increase of the signal level.
  • the impulsive noise probability calculation unit 601 may comprise an initial value setting unit, configured to set an initial value of the impulsive noise probability as the onset probability when the onset probability is higher than a first predetermined probability threshold and a decay determination unit, configured to determine whether the noise signal has a decay trend, wherein the impulsive noise probability calculation unit 601 may further configured to calculate the impulsive noise probability of the noise signal based on whether the noise signal has the decay trend.
  • the decay determination unit may be further configured to perform at least one of: determine whether the noise signal has the decay trend based on a distance between the signal level of the noise signal and a maximal signal level of the noise signal within a first time window; or determine whether the noise signal has the decay trend based on a slope of the noise signal over time.
  • the impulsive noise probability calculation unit 601 may be further configured to increase the impulsive noise probability at a first rate when the noise signal has the decay trend; and decrease the impulsive noise probability at a second rate when the noise signal has no decay trend.
  • system 600 may further comprise a maximum decreasing unit, configured to decrease the maximal signal level when the onset probability is lower than a second predetermined probability threshold and a distance between the current estimated level of the noise signal and the signal level of the noise signal is lower than a predetermined distance threshold.
  • the system 600 may further comprise an impulse establishment time recording unit, configured to record an impulse establishment time when beginning to calculate the impulsive noise probability, wherein the impulse establishment time recording unit may be further configured to set the impulse establishment time to zero when the onset probability is higher than the first predetermined threshold.
  • the smoothing factor determination unit 602 may be further configured to determine the variable smoothing factor based on a reference smoothing factor and a maximum of the impulsive noise probability and the onset probability, when the impulse establishment time is lower than a predetermined time threshold; and determine the variable smoothing factor based on the reference smoothing factor and the impulsive noise probability, when the impulse establishment time is higher than or equal to the predetermined time threshold.
  • the variable smoothing factor when determining the variable smoothing factor, is a decreasing function of the impulsive noise probability over time.
  • the noise level estimation unit 603 may be further configured to smooth the noise signal with the variable smoothing factor; and determine a smoothed signal level of the smoothed noise signal as the current estimated level of the noise signal.
  • the system 600 may further comprise a minimum determination unit, configured to determine a minimal signal level of the noise signal within a second time window.
  • the noise level estimation unit 603 may be further configured to smooth the noise signal with the variable smoothing factor; and select a maximum of a smoothed signal level of the smoothed noise signal and the minimal signal level as the current estimated level of the noise signal.
  • the system 600 may further comprise a stability degree determination unit, configured to determine a degree of stability of the noise signal; and a time window narrowing unit, configured to narrow down the second time window when the degree of stability is lower than a predetermined stability threshold and the impulse establishment time is lower than the predetermined time threshold, such that the minimal signal level of the noise signal within the narrowed second time window is larger than the smoothed signal level.
  • a stability degree determination unit configured to determine a degree of stability of the noise signal
  • a time window narrowing unit configured to narrow down the second time window when the degree of stability is lower than a predetermined stability threshold and the impulse establishment time is lower than the predetermined time threshold, such that the minimal signal level of the noise signal within the narrowed second time window is larger than the smoothed signal level.
  • the degree of stability may be measured by a variance or standard deviation of the noise signal within a predetermined measurement time window.
  • the components of the system 600 may be a hardware module or a software unit module.
  • the system 600 may be implemented partially or completely with software and/or firmware, for example, implemented as a computer program product embodied in a computer readable medium.
  • the system 600 may be implemented partially or completely based on hardware, for example, as an integrated circuit (IC), an application-specific integrated circuit (ASIC), a system on chip (SOC), a field programmable gate array (FPGA), and so forth.
  • IC integrated circuit
  • ASIC application-specific integrated circuit
  • SOC system on chip
  • FPGA field programmable gate array
  • FIG. 7 shows a block diagram of an example computer system 700 suitable for implementing embodiments of the present invention.
  • the computer system 700 comprises a central processing unit (CPU) 701 which is capable of performing various processes in accordance with a program stored in a read only memory (ROM) 702 or a program loaded from a storage section 708 to a random access memory (RAM) 703.
  • ROM read only memory
  • RAM random access memory
  • data required when the CPU 701 performs the various processes or the like is also stored as required.
  • the CPU 701, the ROM 702 and the RAM 703 are connected to one another via a bus 704.
  • An input/output (I/O) interface 705 is also connected to the bus 704.
  • the following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, or the like; an output section 707 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), or the like, and a loudspeaker or the like; the storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like.
  • the communication section 709 performs a communication process via the network such as the internet.
  • a drive 710 is also connected to the I/O interface 705 as required.
  • a removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 710 as required, so that a computer program read therefrom is installed into the storage section 708 as required.
  • embodiments of the present invention comprise a computer program product including a computer program tangibly embodied on a machine readable medium, the computer program including program code for performing methods 200.
  • the computer program may be downloaded and mounted from the network via the communication section 709, and/or installed from the removable medium 711.
  • various example embodiments of the present invention may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of the example embodiments of the present invention are illustrated and described as block diagrams, flowcharts, or using some other pictorial representation, it will be appreciated that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
  • embodiments of the present invention include a computer program product comprising a computer program tangibly embodied on a machine readable medium, the computer program containing program codes configured to carry out the methods as described above.
  • a machine readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • the machine readable medium may be a machine readable signal medium or a machine readable storage medium.
  • a machine readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • machine readable storage medium More specific examples of the machine readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CD-ROM portable compact disc read-only memory
  • magnetic storage device or any suitable combination of the foregoing.
  • Computer program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer program codes may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor of the computer or other programmable data processing apparatus, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented.
  • the program code may execute entirely on a computer, partly on the computer, as a stand-alone software package, partly on the computer and partly on a remote computer or entirely on the remote computer or server.
  • EEEs enumerated example embodiments
  • a method for estimating non-stationary noise levels which includes:
  • EEE 2 The estimator according to EEE 1, wherein the impulsive noise probability is estimated using:
  • EEE 3 The smoothing method according to EEE 2, wherein the decaying nature of the impulsive noise is measured by the distance between the instantaneous or smoothed noise level and the noise level output from a maximum tracker.
  • EEE 4 The smoothing method according to EEE 2, wherein the decaying nature of the impulsive noise is measured by calculating the slope (gradient) or smoothed slope of magnitude over time.
  • EEE 5 The method according to EEE 1, wherein features are extracted to judge the likelihood of an abrupt increase of noise floor.
  • EEE 6 The method according to EEE 4, including variance or standard deviation of magnitude over time.
  • EEE 7 The method according to EEE 1, wherein the tracking speeds up by using minimum tracker.
  • EEE 8 The method according to EEE 1, wherein the tracking speeds up by remembering one or a few previous estimation levels from quick smoothers.
  • EEE 9 The method according to EEE 7, wherein the length of the minimum tracking window is controlled by judging the likelihood of an abrupt increase of noise floor.
  • a system for noise level estimation comprising:
  • EEE 11 The system according to EEE 10, wherein the noise signal is a signal in one of a plurality of frequency bands of a noise input signal, or is a broadband signal of the noise input signal; wherein if the calculated impulsive noise probability for at least one frequency band of the noise input signal is higher than a confidence threshold, the impulsive noise probabilities for the remaining frequency bands are increased.
  • EEE 12 The system according to EEE 10 or EEE 11, further comprising:
  • EEE 13 The system according to EEE 12, wherein the impulsive noise probability calculation unit comprising:
  • EEE 14 The system according to EEE 13, wherein the decay determination unit is further configured to perform at least one of:
  • EEE 15 The system according to EEE 13 or EEE 14, wherein the impulsive noise probability calculation unit is further configured to:
  • EEE 16 The system according to EEE 14, further comprising:
  • EEE 17 The system according to any of EEEs 10to 16, further comprising:
  • EEE 18 The system according to EEE 17, wherein the smoothing factor determination unit is further configured to:
  • EEE 19 The system according to any of EEEs 10 to 18, wherein the noise level estimation unit is further configured to:
  • EEE 20 The system according to any of EEEs 10 to 18, further comprising:
  • EEE 21 The system according to EEE 20, further comprising:
  • EEE 22 The system according to EEE 21, wherein the degree of stability is measured by a variance or standard deviation of the noise signal within a predetermined measurement time window.

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  • 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)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
EP14179096.4A 2014-06-09 2014-07-30 Geräuschpegelschätzung Withdrawn EP2980800A1 (de)

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EP14179096.4A EP2980800A1 (de) 2014-07-30 2014-07-30 Geräuschpegelschätzung
PCT/US2015/034733 WO2015191470A1 (en) 2014-06-09 2015-06-08 Noise level estimation
EP15729062.8A EP3152756B1 (de) 2014-06-09 2015-06-08 Geräuschpegelschätzung
US15/316,092 US10141003B2 (en) 2014-06-09 2015-06-08 Noise level estimation

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EP3253074A1 (de) * 2016-05-30 2017-12-06 Oticon A/s Hörgerät mit einer filterbank und einem einsetzdetektor
US10321243B2 (en) 2016-05-30 2019-06-11 Oticon A/S Hearing device comprising a filterbank and an onset detector
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EP3340642A1 (de) * 2016-12-23 2018-06-27 GN Hearing A/S Hörgerät mit schallimpulsunterdrückung und zugehöriges verfahren
EP3917157A1 (de) * 2016-12-23 2021-12-01 GN Hearing A/S Hörgerät mit schallimpulsunterdrückung und zugehöriges verfahren
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