JP2023001605A - wireless communication device - Google Patents

Abstract

To make it possible to increase a call limit distance while taking measures against deterioration of received voice due to a call distance in the prior art.SOLUTION: A wireless communication device of the present invention is a wireless communication device including a reception demodulation unit that demodulates a received wireless signal into an audio signal. The wireless communication device includes a noise cancellation unit that performs audio signal processing so as to cancel noise from the demodulated audio signal. The noise cancellation unit performs noise cancellation processing, using a deep learning model unit that is learned using a feature parameter of human voice and that determines whether it is a non-voice period containing noise from the demodulated audio signal. Here, the received wireless signal is a wireless signal modulated by a frequency modulation method or a phase modulation method according to a predetermined audio signal.SELECTED DRAWING: Figure 1

Description

TECHNICAL FIELD The present invention relates to a radio communication apparatus for a specific low-power radio communication radio station using, for example, FM modulation.

For example, in the wireless communication device disclosed in Patent Document 1, in a wireless communication device having a noise canceling function and a squelch function, squelch occurs near the squelch sensitivity even though the noise canceling function works and the reception sensitivity is good. In order to prevent the door from being closed, the following configuration is disclosed.

A demodulator of the wireless communication device demodulates the IF signal frequency-converted by the frequency converter and outputs a demodulated signal. The noise cancellation unit outputs a noise cancellation signal obtained by suppressing the noise component of the demodulated signal. The output unit outputs the demodulated signal as audio when the noise canceling function is off, and outputs the noise canceling signal as audio when the noise canceling function is on. The squelch control section stops audio output from the output section when the voltage value indicated by the noise signal is equal to or greater than the first threshold, and permits audio output from the output section when the voltage value is less than the second threshold. The first threshold and the second threshold when the noise canceling function is on are set to be higher than when the noise canceling function is off.

FIG. 5 is a block diagram showing a configuration example of a radio device 100 according to a conventional example. 5, the wireless device 100 includes a control unit 10, a receiving antenna 11, a receiving demodulating unit 12, a noise amount and electric field strength detecting unit 16, and a received audio muting unit 17 using noise squelch (also called a noise squelch circuit). , an audio signal amplifier 14 and a speaker 15 .

In FIG. 5, a radio signal received by a receiving antenna 11 is input to a receiving demodulator 12 . The reception demodulation unit 12 performs signal processing such as low noise amplification, low frequency conversion, intermediate frequency amplification, and FM (frequency modulation) demodulation on the input radio signal, thereby demodulating the audio signal. The received sound is muted by noise squelch and output to the speaker 15 via the audio signal amplifier 14 . The noise amount and electric field intensity detection unit 16 detects the noise amount and electric field intensity of the received radio signal and outputs them to the control unit 10 . The control unit 10 operates the received audio muting unit 17 by noise squelch according to the detected amount of noise and electric field intensity to mute the output audio signal. Specifically, when the electric field intensity is less than a predetermined threshold value and the noise amount is equal to or greater than a first threshold value, the control unit 10 operates the reception audio mute unit 17 by noise squelch to mute the audio signal. The output of the signal is stopped, and when it becomes less than the second threshold value, the noise squelch-based reception audio mute section 17 is brought into a non-operating state to permit the output of the audio signal.

Japanese Patent No. 6730591

FIG. 6 is a block diagram showing an operation example of a wireless communication system including the plurality of wireless devices 100A-100D of FIG. In FIG. 6, radios 100A to 100D each having the configuration of radio 100 are arranged within a predetermined communication limit distance (coverage area). shall be placed.

In the conventional FM modulation type specified low-power wireless communication device, when the electric field strength decreases as the communication distance increases, the state becomes close to no signal, and the white noise output when there is no signal is mixed with the received voice, resulting in As the wireless device 100C shown in FIG. 6 approaches the call limit distance, the received voice becomes mixed with noise, making it difficult to distinguish the voice.

A general noise squelch circuit installed in these conventional wireless communication devices filters and rectifies this noise, thereby determining the noise level in the receiver and stopping the received voice. . However, in the case of this method, since the sound that can be originally received is also stopped due to the relationship between the noise sound and the received sound, the wireless device 100D in FIG. It becomes the current state of the machine.

SUMMARY OF THE INVENTION An object of the present invention is to solve the above problems and to provide a wireless communication device and a wireless communication system capable of improving the call limit distance while taking measures against the deterioration of the received voice due to the call distance in the conventional technology. .

A radio communication apparatus according to an aspect of the present invention is a radio communication apparatus including a reception demodulator that demodulates a received radio signal into an audio signal,
A noise canceling unit that performs audio signal processing to cancel noise from the demodulated audio signal,
The noise cancellation unit performs noise cancellation processing using a deep learning model unit that is learned using characteristic parameters of human speech and determines whether or not it is a non-speech period containing noise from the demodulated speech signal. I do.

Therefore, according to the wireless communication device of the present invention, instead of the noise squelch circuit according to the conventional technology, the noise cancellation unit that performs noise cancellation processing using the deep learning model unit is provided. It is possible to improve the communication limit distance while taking measures against deterioration of received voice.

1 is a block diagram showing a configuration example of a wireless device 1 according to an embodiment; FIG. 2 is a block diagram showing a configuration example of a noise cancellation unit 13 of FIG. 1; FIG. 3 is a block diagram showing a configuration example of a deep learning model unit 35 of FIG. 2; FIG. 2 is a block diagram showing an operation example of a radio communication system including a plurality of radios 1A to 1D of FIG. 1; FIG. FIG. 2 is a block diagram showing a configuration example of a wireless device 100 according to a conventional example; 6 is a block diagram showing an operation example of a wireless communication system including the plurality of radios 100A to 100D of FIG. 5; FIG.

Hereinafter, embodiments and modifications according to the present invention will be described with reference to the drawings. In addition, the same code|symbol is attached|subjected about the same or similar component.

FIG. 1 is a block diagram showing a configuration example of a wireless device 1 according to an embodiment. In FIG. 1, a wireless device 1 is an example of a wireless communication device, and includes a receiving antenna 11, a receiving demodulation unit 12, a noise canceling unit 13, an audio signal amplifier 14, a speaker 15, a control unit 20, a PTT It comprises an operation unit 21 including a (Push To Talk) key 21A, a microphone 22, an audio signal amplifier 23, a modulation transmission unit 24, and a transmission antenna 25.

Here, the wireless device 1 according to the embodiment is an example of a wireless communication device of a specified low-power wireless station for a specified low-power wireless communication system, for example. In this embodiment, in the receiving section of the wireless device 1, instead of the reception audio mute section 17 by noise squelch in FIG. It is characterized by having a noise canceling section 13 . Also, a plurality of wireless devices 1 constitute a wireless communication system.

In FIG. 1, a radio signal received by a receiving antenna 11 is input to a receiving demodulator 12 . The reception demodulation unit 12 performs low-noise amplification, low-frequency conversion, intermediate frequency amplification, etc. on the received radio signal, and then performs a predetermined demodulation method such as FM (frequency modulation) demodulation or PM (phase modulation) demodulation. demodulates it into an audio signal and outputs it to the noise canceling unit 13 . The noise canceling unit 13 uses a deep learning model unit 35 (FIG. 3) that has been deep-learned using human speech to allow the demodulated audio signal to pass through only during the audio signal period, thereby canceling noise. After the audio signal is processed, the processed audio signal is output to the speaker 15 via the audio signal amplifier 14 .

The microphone 22 converts an input voice into a voice signal and outputs the voice signal to the modulation transmitter 24 via the voice signal amplifier 23 . The control unit 20 operates the modulation transmission unit 24 when the PTT key 21A is turned on, and the modulation transmission unit 24 modulates the radio carrier wave according to the input audio signal with a predetermined modulation method, and then modulates the modulated radio wave. A radio signal, which is a carrier wave, is transmitted from the transmitting antenna 25 after high frequency conversion and power amplification.

In this embodiment, the wireless device 1 operates in a simultaneous call system in which the transmission frequency and the reception frequency are different, but the present invention is not limited to this, and the wireless device 1 uses the same transmission frequency and reception frequency. frequency is used, the controller 20 stops the operation of the reception demodulator 12 when the PTT key 21A is turned on.

Next, with reference to FIG. 2, the configuration and operation of the noise cancellation unit 13 of FIG. 1 using the deep learning model unit 35 will be described below.

FIG. 2 is a block diagram showing a configuration example of the noise cancellation section 13 of FIG.

Here, the term "phoneme" refers to the unit of sound that distinguishes one word from another in a particular language, and the term "vibration rate" refers to the zero point of the digitized vibration data at each second. and 1, and the term "vibration count (VC)" refers to the sum of the values of the digitized vibration data within each frame. Further, the "vibration pattern" means the data distribution of the sum of the vibration frequencies calculated for each predetermined number of frames along the time axis. In the deep learning model unit 35, noise cancellation processing is performed in consideration of different vibration patterns, that is, differences in the data distribution of the sum of different vibration count values (VS values), and the vibration rate is similar to the vibration count value. However, the higher the vibration rate, the higher the vibration count.

Both the amplitude and vibration rate of the speech signal are observable. A feature of the noise cancellation unit 13 is that it detects an audio event according to the amplitude and vibration rate of the audio signal. Another feature is to distinguish between speech and non-speech/silence by measuring the sum of the vibration count values of the digitized vibration data for a predefined number of frames. Another feature is to classify the input audio signal data stream into different phonemes according to their vibration patterns. Another feature is the correct discrimination of the first activation phoneme from the incoming audio signal data stream to trigger downstream processing units, thereby calculating power consumption, etc. of the computing system containing the processing units. It is to save costs.

In FIG. 2, the noise cancellation unit 13 performs noise cancellation processing using audio event detection, and includes an audio signal preprocessing unit 38, an AD converter 39, and an audio signal processing unit 30. be done. Here, the audio signal preprocessing unit 38 performs audio signal preprocessing, including high-pass filtering, low-pass filtering, amplification, or a combination thereof, on the analog audio signal, and outputs the processed analog audio signal. Output to AD converter 39 . That is, the audio signal pre-processing unit 38 allows the audio signal from the microphone 22 to pass only within a predetermined level range of human audio signals and in a predetermined bandwidth. Next, the AD converter 39 AD-converts the analog audio signal into a digital audio signal according to a predetermined reference voltage Vref and an allowable voltage Vadm (<Vref), and outputs the digital audio signal to the input interface 36 of the audio signal processing section 30 .

In the present embodiment, in the AD converter 39, the allowable voltage Vadm smaller than the reference voltage Vref is combined with the reference voltage Vref to obtain the first threshold voltage Vth1 (=Vref+Vadm)) and the second threshold voltage Vth2 (=Vref−Vadm), and the AD converter 39 converts the first threshold voltage Vth1 based on the first threshold voltage Vth1 and the second threshold voltage Vth2. Noise and interference in the input analog audio signal are removed by not performing AD conversion on noise above or below the second threshold voltage Vth2, but by performing AD conversion on the audio signal between them. can do. Here, if Vref=1.0 V and Vadm=0.01 V, for example, it can be understood that the frequency of vibration data is low in a quiet environment, and the frequency of vibration data is high in a voice environment. In this embodiment, the “frame size” means the number of sampling points corresponding to the digitized vibration data in each frame, and the “phoneme window Tw” means the speech feature amount of each phoneme. Means time to collect. In a preferred embodiment, the duration Tf of each frame is eg 0.1-1 milliseconds (ms) and the phoneme window Tw is eg about 0.3 seconds. In a further preferred embodiment, the number of sampling points corresponding to the digitized vibration data within each frame ranges, for example, from 1-16.

When analyzing speech signals, the method of short-term analysis is usually adopted since most speech signals are stable in a short period of time. For example, assuming that the sampling frequency fs used by the AD converter 39 is 16000 and the duration Tf of each frame is 1 ms, the frame size is fs×1/1000=16 sample points.

In FIG. 2, the audio signal processing unit 30 is configured by, for example, a computer device,
(1) A CPU (Central Processing Unit) 31 that executes predetermined audio signal processing such as noise cancellation;
(2) a ROM (Read Only Memory) 32 that stores an operating system that executes the basic processing of the CPU 31, the audio signal processing program, and data necessary for executing the program;
(3) a RAM (Read Access Memory) 33 for storing data being processed when the operating system for executing the basic processing of the CPU 31 and the program for the audio signal processing are executed;
(4) a non-volatile EEPROM (Electrically Erasable Programmable Memory) 34 for storing later-described setting data necessary for executing the audio signal processing;
(5) For example, it is composed of a neural network, etc., and removes noise from voice signal data that is input after deep learning based on human voice signal data, and extracts and outputs substantially only the voice signal. a deep learning model unit 35;
(6) an input interface 36 that performs a predetermined signal conversion process for converting audio signal data input from the AD converter 39 into a signal specification value in the subsequent stage and outputs the result to the CPU 31;
(7) The audio signal data from which noise has been removed by the deep learning model unit 35 is subjected to a predetermined signal conversion process for converting it into a signal specification value in the subsequent stage, and the wireless device 1 is transmitted through the terminal T12, the audio line L2, etc. an output interface 37 that outputs to
configured with

Here, the EEPROM 34 stores, for example, a series of vibration count values VC, a sum of vibration count values VS, a sum of vibration count values VSf, a sum of vibration count values VSp (to be described later), and sound feature values of all feature vectors. do. Note that the EEPROM 34 may be a storage device such as an external memory. The audio event detection method applied to the audio signal processor 30 is executed during runtime by the CPU 31 to capture audio events. Audio event detection is performed assuming fs=16000, Tf=1 ms, and Tw=0.3 s.

Specifically, the CPU 31 calculates the sum of the vibration data values of the current frame (that is, within 1 ms) to be processed, acquires the vibration count value VC, and then obtains the VC of the current frame at time Tj. Store the value in EEPROM 34 . Here, the vibration count values VC of x frames are added to obtain the sum of the vibration count values VS of the current frame at time Tj. x frames includes the current frame. In one embodiment, the CPU 31 adds the vibration count value VC of the current frame at the time Tj and the sum VSp of the vibration count values of the (x−1) frames immediately before that frame to obtain the x number of vibration count values at the time Tj. Obtain the sum VS (=VC+VSp) of the vibration count values of the frame.

In the modified example, the CPU 31 outputs the vibration count value VC of the current frame at the time Tj, the total sum VSf of the vibration count values of the y frames immediately after that, and the (xy-1) frames immediately before that. to obtain the sum of vibration counts VS (=VC+VSf+VSp) of x frames at time Tj, where y is greater than or equal to zero. The CPU 31 stores the values of VS, VSf and VSp in the EEPROM 34 . In the preferred embodiment, the duration of x frames (phoneme window Tw) (x×Tf) is approximately 0.3 seconds. In a further preferred embodiment, the number of sampling points corresponding to x frames of digitized vibration data is in the range of x to 16x.

In general, with respect to speech signal data, the vibration patterns of the vibration count values VC are similar for the same phoneme, but the vibration patterns of the VS values are completely different for different phonemes. Therefore, the vibration pattern of the vibration count value VC can be used to distinguish between phonemes. In particular, for example, the barking of a chicken or a cat and human speech are completely different in terms of the frequency distribution of the vibration coefficient VC, and it is known that most of the vibration coefficient VC of human speech is distributed below 40. .

In the learning phase, the CPU 31 of the speech signal processing unit 30 first executes a predetermined speech signal data collection method a plurality of times to collect a plurality of feature vectors corresponding to a plurality of phonemes, and labels corresponding to the plurality of feature vectors. Append to form multiple labeled training examples. A plurality of labeled training examples for different phonemes, including the starting phoneme, are then applied to training the deep learning model unit 35 . Finally, a trained deep learning model unit 35 (constituting a predictive model for the speech signal data) is created to classify whether the incoming speech signal data stream contains the activation phoneme. When a predetermined phoneme is specified as a starting phoneme for the audio signal processing unit 30, the deep learning model unit 35 is trained with a plurality of labeled training examples for different phonemes including at least the specified phoneme.

That is, in the training phase, the deep learning model portion 35 is trained using a set of labeled training examples, whereby the deep learning model portion 35 learns the three audio features of each frame of the labeled training examples. Based on the quantity (eg, (VSj, TDj, TGj)), we try to recognize a given activation phoneme between j=0-299. At the end of the learning phase, the trained deep learning model unit 35 provides a learned score corresponding to the activation phoneme, which is then converted at runtime to the incoming audio signal data stream. Used as criteria for classification. VSj, TDj, and TGj are defined as follows.
(1) VSj: sum of vibration count values (VS value) of frame j;
(2) TDj: the time period of the sum of non-zero vibration counts (VS values) at frame j; and (3) TGj: the time between the sums of non-zero vibration counts (VS values) at frame j. Gap (time gap).

Various machine learning techniques related to supervised learning can be used to train the deep learning model portion 35, such as support vector machine (SVM) methods, random forest methods, convolutional neural network methods, etc. can. In supervised learning, a function calculator (i.e., deep learning model portion 35) is created using a plurality of labeled training examples, each of which consists of an input feature vector and a labeled output. When trained, deep learning model portion 35 can be applied to new unlabeled examples to generate corresponding scores or predictions.

FIG. 3 is a block diagram showing a detailed configuration example of the deep learning model unit 35 of FIG.

The deep learning model unit 35 is implemented using a neural network, as shown in FIG. 3, for example. Here, the neural network comprises one input layer 41 , at least one and preferably several intermediate layers 42 and one output layer 43 . The input layer 41 has three input neurons 51, 52, 53, each input neuron 51, 52, 53 corresponding to three audio feature values (i.e., VSj, TDj, TGj) of each frame of the feature vector. . The intermediate layer 42 is also composed of neurons 61-74 having weight coefficients associated with each input neuron 51, 52, 53 and bias coefficients for each neuron. By changing the weighting and biasing factors of each neuron 61-74 of the hidden layer 42 through cycles of the learning phase, the neural network can be trained to report a predicted value for a given type of input. . In addition, the output layer 43 includes one output neuron 81 that provides one prediction value corresponding to a phoneme (specifically indicating whether it is a speech period or a non-speech period containing noise).

As described above, in the noise cancellation unit, the deep learning model unit 35 learns using the feature parameters of human speech, and determines whether or not it is a non-speech period containing noise from the input speech signal. do. Then, the CPU 31 of the audio signal processing unit 30 performs noise cancellation processing so as not to pass the non-speech period containing noise from the input audio signal based on the determination of the deep learning model unit 35, and performs the noise cancellation. Outputs the processed audio signal. Here, the deep learning model unit 35 receives as input the characteristic parameters of human speech, and outputs the determination result of determining whether or not the input speech signal is a non-speech period containing noise. It consists of a neural network.

FIG. 4 is a block diagram showing an operation example of a radio communication system including the plurality of radios 1A to 1D of FIG. In FIG. 4, radios 1A to 1D each having the configuration of radio 1 are arranged within a predetermined communication limit distance (coverage area), and among them, radios 1A to 1C are within a predetermined communication limit distance with noise squelch. shall be placed.

As shown in FIG. 4, by passing the demodulated audio signal through the noise canceling unit 13 before outputting it to the speaker 15, white noise is eliminated when there is almost no signal, and the radio 1C in FIG. Even when approaching the limit distance, the received voice continues to keep clear voice. In addition, instead of the conventional noise squelch circuit, by canceling noise using the noise canceling unit 13 that uses the deep learning model unit 35, the output of noise in the no-signal state is stopped (or reduced). The output of the audio signal that can be originally received is not stopped. Therefore, the radio device 1D in FIG. 4 can output the received audio signal to the very limit of the communication distance, and the communication distance can be extended compared to the noise squelch circuit.

As described in detail above, according to the wireless communication device according to the present invention, noise is canceled using the noise canceling unit 13 using the deep learning model unit 35 instead of the conventional noise squelch circuit. It is possible to improve the call limit distance while taking measures against the deterioration of the received voice due to the call distance.

1, 1A to 1D Wireless device 11 Receiving antenna 12 Receiving demodulator 13 Noise canceler 14 Audio signal amplifier 15 Speaker 16 Noise amount and electric field strength detector 17 Received audio mute unit 20 by noise squelch Control unit 21 Operation unit 21A PTT key 22 Microphone 23 Audio signal amplifier 24 Modulation transmitter 25 Transmission antenna 30 Audio signal processor 31 CPU
32 ROMs
33 RAM
34 EEPROMs
35 deep learning model unit 36 input interface 37 output interface 38 audio signal preprocessing unit 39 AD converter 41 input layer 42 intermediate layer 43 output layer 51 to 81 neurons 100, 100A to 100D wireless device

Claims (6)

A wireless communication device comprising a receiving demodulator that demodulates a received wireless signal into an audio signal,
A noise canceling unit that performs audio signal processing to cancel noise from the demodulated audio signal,
The noise cancellation unit performs noise cancellation processing using a deep learning model unit that is learned using characteristic parameters of human speech and determines whether or not it is a non-speech period containing noise from the demodulated speech signal. wireless communication device. 2. The radio communication device according to claim 1, wherein the received radio signal is modulated by a frequency modulation method or a phase modulation method according to a predetermined voice signal. The noise cancellation unit, based on the determination of the deep learning model unit, performs noise cancellation processing so as not to pass a non-speech period containing noise from the input audio signal, and the audio signal after the noise cancellation processing. provided with an audio signal processing unit that outputs
The radio communication device according to claim 1 or 2. The noise cancellation unit is
Further comprising an audio signal pre-processing unit provided in the preceding stage of the audio signal processing unit for passing only an input audio signal within a predetermined level range of a human audio signal and a predetermined bandwidth. 4. The wireless communication device of claim 3. The deep learning model unit is composed of a predetermined neural network that receives characteristic parameters of human speech as an input and outputs a determination result that determines whether or not the input speech signal is a non-speech period containing noise. The radio communication device according to any one of claims 1 to 4, wherein the radio communication device is The wireless communication device according to any one of claims 1 to 5, wherein said wireless communication device is a specified low power radio station which is a wireless communication device for a specified low power wireless communication system.

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