PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Sound quality improvement for speech acquisition based on deeplearning and harmonic reconstruction with laser microphone
Shoji UEDA1; Kenta IWAI2; Takahiro FUKUMORI3; Takanobu NISHIURA4
1

Graduate School of Information Science and Engineering, Ritsumeikan University, Japan2, 3, 4

College of Information Science and Engineering, Ritsumeikan University, Japan

ABSTRACTA laser microphone has recently been focused on acquiring the distant target speech without unnecessary
sounds. It measures the vibration of the object near the target sound source by irradiating the object surface
with the laser beam. However, the speech acquired with this microphone degrades the sound quality. For
instance, the speech components at higher frequencies are attenuated by vibration characteristic of the object,
and the speech is collapsed by the stationary noise due to lower intensity of the laser beam reflected from theobject. To improve the sound quality of the degraded speech, deep neural networks (DNNs) have recently
been proposed. They are trained by using a set of acoustic features extracted from the degraded speech andthe clean speech. However, the speech components at higher frequencies are still attenuated after processedby the DNN. Therefore, we propose the method to improve the sound quality with harmonic reconstruction
after processed by the DNN. The proposed method is based on the harmonic structure of the speech signal
and reconstructs the speech harmonics by utilizing a non-linear function. We evaluated the effectiveness ofthe proposed method through perceptual evaluation of speech quality.
Keywords: Laser microphone, Deep learning, Deep neural network, Harmonic reconstruction, Sound quality
improvement

1. INTRODUCTION
When the distant target speech is acquired with the air-conduction microphone, unnecessary sounds
are also acquired because the diaphragm of the microphone is vibrated by not only the target speech
but also unnecessary sounds. Recently, a laser microphone has been focused on the solution of thisproblem [1]. It measures the object’s vibration by irradiating the object surface with the laser beam.
Because of the measurement using the laser beam, it can measure the only target speech as far as thelaser beam reaches the object. However, the speech acquired with the laser microphone degrades thesound quality. For example, the speech components at higher frequencies are attenuated, and the
speech is collapsed by the stationary noise. These degradations are caused by vibration characteristicof the object and lower intensity of the laser beam reflected from the object. By these degradations, thespeech harmonics at higher frequencies are lost, and the acquired speech becomes unclear. Therefore,
it is necessary to improve the sound quality of the speech acquired with the laser microphone.
Recently, deep learning has attracted attention as the sound quality improvement of the degradedspeech [2-4]. The basic strategy of applying a deep neural network (DNN) for the sound quality
improvement is to train a network using a large set of acoustic features extracted from the degradedspeech and the clean speech. The trained DNN can reduce the nonstationary noise as well as thestationary noise. However, if the input signal is close to zero, the output signal is also close to zero.
Hence, when the DNN is trained using the speech with high-frequency attenuation like the speechacquired with the laser microphone, the DNN can ineffectively reconstruct the speech components at
higher frequencies. Moreover, the larger the size of the DNN is, the larger the computational
complexity of applying the DNN is.
In this paper, we propose a method for sound quality improvement to reconstruct the harmonic1

is0247es@ed.ritsumei.ac.jp
iwai8sp@fc.ritsumei.ac.jp
3
fukumori@fc.ritsumei.ac.jp
4nishiura@is.ritsumei.ac.jp
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structure of the speech after processed by the DNN. The proposed method is based on the harmonic
structure of the speech signal and reconstructs the speech harmonics by utilizing a non -linear function.
To reconstruct the harmonic structure, we focus on that the periodic square wave which composes the
harmonics of the fundamental frequency [5, 6]. The convolution between the spectrum of the acquired
speech and that of the periodic square wave can reconstruct the speech harmonics. Then, the soundquality of the speech signal is improved, in other words, the speech signal is clearer. Moreover, thereconstruction of the harmonic structure can be employed with small configuration of the filter, and the
proposed method has small computational complexity. Therefore, we can reduce the computational
complexity for the sound quality improvement by combining the smaller DNN with the harmonicreconstruction.

2. SPEECH ACQUISITION WITH LASER MICROPHONE
In this section, we describe the degradation of the speech acquired with the laser microphone. Here,
we define the laser microphone as the system for acquiring the sound by irradiating the surface of theobject near the sound source with the laser beam. In this paper, we use a laser Doppler vibrometer(LDV) as the laser microphone. It can measure the vibration of the object which is vibrated slightly by
the sound. It can be used to acquire the distant speech by irradiating the distant object with the laser
beam. However, the speech acquired with the laser microphone is degraded by various noises such as
the stationary noise [7] and the speckle noise [1, 8]. The stationary noise occurs when the intensity of
the laser beam reflected from the object becomes lower. The spectral amplitude of this noise is
proportional to the frequency. The speckle noise occurs in the vibration measurements on roughsurfaces which are moved to vertical direction with respect to the irradiating direction. It has spikes
which direct towards zero in the time domain. Moreover, the speech components at higher frequenciesof the acquired speech are attenuated. This is caused by vibration characteristic of the object that thelaser microphone irradiates with the laser beam.

(a)

(b)

(c)
Figure 1 – Arrangement for measurement with the laser microphone using (a) a fixed paper,(b) a paper moved by the wind and (c) a polypropylene plate with a reflective tape

(a)

(b)

(c)

(d)

Figure 2 – Spectrograms of (a) clean speech, acquired speech by the laser microphone using(b) a fixed paper, (c) a paper moved by the wind and (d) a polypropylene plate with a reflective tape6938

Here, we show the measurement results to confirm the degradation of the speech acquired with thelaser microphone. In this experiment, we focused on the degradation by the stationary noise, thespeckle noise and high-frequency attenuation. Experimental setup is shown in Fig. 1. To confirm thestationary noise, we used a paper located in front of the loudspeaker. A paper reflects the laser beamwith lower intensity. To confirm the speckle noise, we used a paper moved by the wind from thecirculator. A paper which has rough surfaces is moved by the wind to vertical direction with respect tothe irradiating direction. To confirm high-frequency attenuation, we used a polypropylene plate with areflective tape. The vibration measured by a polypropylene plate has lower power at higher
frequencies [9]. A reflective tape is used for suppressing the noise in the measurement with the laser
microphone. We acquired Japanese speech “ikioi (Japanese pronunciation: ikioi)” that the femaleuttered in ATR phoneme balanced 216 words [10]. The spectrograms of acquired speech signals areshown in Fig. 2. From Fig. 2, we can see that the speeches acquired with the laser microphone aredegraded. The acquired speech with the fixed paper is collapsed by the stationary noise over the speechcomponents at higher frequencies. The speech acquired by using the paper moved by the wind iscollapsed by the speckle noise which randomly occurs on the entire frequency range. In the acquired
speech by using the polypropylene plate with the reflective tape, the noise is reduced compared withthe acquired speeches of the object without the reflective tape. On the other hand, the speechcomponents at higher frequencies attenuates because of the vibration characteristic of the object. Fromthese results, we confirmed the degradation by acquiring with the laser microphone. Thus, it isrequired to reduce the noises, whereas it is also required to reconstruct the speech components at
higher frequencies of speech for increase of sound quality.

3. SOUND QUALITY IMPROVEMENT BASED ON DEEP NEURAL NETWORK
Recently, DNNs have become popular for speech quality improvement. They are trained by a set of
acoustic features extracted from the degraded speech and the clean speech. Figure 3 shows the trainingof the regression based DNN framework. In this paper, our purpose is sound quality improvement forthe speech acquired with the laser microphone. To achieve our purpose, the DNN should be trained tolearn the mapping from the acquired speech features to the clean speech ones. To improve the soundquality, various speech features have been proposed [2-4]. In the speech quality improvement, they canbe converted back to the waveform in the time domain. For instance, log-power spectrum is popular as
an acoustic feature for the deep learning. It can effectively learn the speech components which havesmall power at higher frequencies because it is used on the logarithmic scale. To obtain the improved
speech, the amplitude spectrum is combined with the phase spectrum of the acquired speech . In
addition, short-time waveform is also used as acoustic feature for the deep learning. It can learn the
mapping function including phase information. Because of this, the DNN trained using short-time
waveform can directly obtain the improved speech without phase replacement. For effective training
the DNN, the concatenated frames are used as inputof time.
p to learn over a periodpFigure 3 – Training of the regression based DNN framework

Figure 4 – Application of the regression based DNN framework6939

Figure 4 shows the application of the trained DNN. The clean speech features are estimated frameby frame from the acquired speech. However, the speech components at higher frequencies are still
attenuated after processed by the DNN. This is because the output signal is close to zero when the
input signal is close to zero. Because of this attenuation, the speech improved by the DNN becomes
still unclear. To make the improved speech clear, the speech is required to reconstruct the components
at higher frequencies. On the other hand, the larger the size of the DNN is, the larger thecomputational complexity of applying the DNN is. To suppress the computation load, it is necessary
to utilize the function with simple configuration.

4. SOUND QUALITY IMPROVEMENT BASED ON DEEP NEURAL NETWORKAND HARMONIC RECONSTRUCTION
After the regression based DNN presented in the previous section, the noises acquired with the laser
microphone are suppressed effectively. However, the processed speech still attenuates components at
higher frequencies. This indicates that the speech loses harmonic structure which represents the speechcharacteristics. To solve this problem, we focus on that the harmonic structure composes the higher
frequencies of the speech [11]. To suppress the computation load, we utilize the harmonicreconstruction with simple configuration of the filter. The noise reduction methods [5, 6] uses a
non-linear function for harmonic reconstruction. By utilizing a non-linear function, the proposed
method reconstructs the speech harmonics which are lost in the acquirement with the laser
microphone.
4.1 Harmonic Reconstruction
The method to reconstruct the speech harmonics consists of applying a non -linear function ሾ‫ڄ‬ሿ
in the time domain. For example, the absolute value, minimum value, or maximum value function
are used as the non-linear function. In this paper, we use a max function as the non-linear functionሾ‫ڄ‬ሿ and the harmonic-reconstructed speech ‫ݔ‬୦ୟ୰୫୭ ሺ݊ሻ is calculated by‫ݔ‬୦ୟ୰୫୭ ሺ݊ሻ ൌ ሾ‫ݔ‬ሺ݊ሻሿ(1)ሾ‫ݔ‬ሺ݊ሻሿ ൌ ሼ‫ݔ‬ሺ݊ሻǡ Ͳሽ ൌ ‫ݔ‬ሺ݊ሻ‫݌‬൫‫ݔ‬ሺ݊ሻ൯(2)ͳሺ‫ݔ‬ሺ݊ሻ ൐ Ͳሻ‫݌‬൫‫ݔ‬ሺ݊ሻ൯ ൌ ൜Ͳሺ‫ݔ‬ሺ݊ሻ ൏ Ͳሻ

(3)

where ‫ݔ‬ሺ݊ሻ is the time waveform of the speech, and ݊ is the discrete time index. Equation (2) is
important to reconstruct the speech harmonics. Figure 5 shows an example of the harmonicreconstruction by Eq. (2). In Fig. 5, the acquired speech is extracted from the speech shown in Fig. 2(d) with a center segment of 20 ms. In Fig. 5, the dotted lines are log-power spectra of the clean
speech. From Fig. 5, we can see that the period of ‫݌‬൫‫ݔ‬ሺ݊ሻ൯ is same as the period of ‫ݔ‬ሺ݊ሻ. Here, it is
known that the periodic rectangular wave ‫ݍ‬ሺ݊ሻ is as following:ͳͳ‫ݍ‬ሺ݊ሻ ൌ ሺʹߨ݂݊ሻ ൅ ሺ͸ߨ݂݊ሻ ൅ ሺͳͲߨ݂݊ሻ ൅ ‫ڮ‬͵ͷஶൌ ෍௠ୀଵͳ൫ሺʹ݉ െ ͳሻ ൈ ʹߨ݂݊൯ʹ݉ െ ͳ

(4)

where ݂ is the normalized frequency of the rectangular wave. Hence, discrete-time Fouriertransform (DTFT) of ‫ݍ‬ሺ݊ሻ is as following:ஶ ሾ‫ݍ‬ሺ݊ሻሿ ൌ ෍ ‫ݍ‬ሺ݊ሻ݁ ି௝ଶగ௙௡ 
ିஶஶͳൌ෍ ܴ൫ሺʹ݉ െ ͳሻ݂൯ߜሺ݂ െ ሺʹ݉ െ ͳሻ݂ሻ
ܰ(5)ܴ൫ሺʹ݉ െ ͳሻ݂൯ ൌ  ൣ൫ሺʹ݉ െ ͳሻ ൈ ʹߨ݂݊൯൧

(6)

௠ୀିஶ

where ߜሺ݉ሻ is the Dirac distribution, ܴሺ݂݉ሻ is the DTFT of the elementary waveform at discrete
frequency ݂݉ and ܰ is the period of the rectangular wave. Equation (5) indicates that the6940

spectrum of the periodic square wave has the power in the components at the harmonics of discrete
frequency ሺʹ݉ െ ͳሻ݂. Because of this characteristic, ‫݌‬൫‫ݔ‬ሺ݊ሻ൯ has the harmonics which include thecomponents at the higher frequencies. From Eqs. (1) and (2), the DTFT of ‫ݔ‬୦ୟ୰୫୭ ሺ݊ሻ is shown as ሾ‫ݔ‬୦ୟ୰୫୭ ሺ݊ሻሿ ൌ  ሾ‫ݔ‬ሺ݊ሻሿ ‫  כ‬ൣ‫݌‬൫‫ݔ‬ሺ݊ሻ൯൧
(7)
Thus, the spectrum of the harmonic-reconstructed speech is the convolution between the spectrum of‫ݔ‬ሺ݊ሻ and the spectrum of ‫݌‬൫‫ݔ‬ሺ݊ሻ൯ which is the components of harmonics. ‫݌‬൫‫ݔ‬ሺ݊ሻ൯ has the same
fundamental frequency as ‫ݔ‬ሺ݊ሻ. This is the reason why the harmonics are reconstructed at theaccurate position with the convolution by Eq. (7). As shown in Fig. 5, the log-power envelope of ൣ‫݌‬൫‫ݔ‬ሺ݊ሻ൯൧ is lower in higher frequencies. For the speech with high-frequency attenuation, thespeech harmonics at higher frequencies are reconstructed using a few harmonics at lower frequencies.
Because of this, the harmonic reconstruction using only Eq. (2) is ineffective to reconstruct the
speech harmonics at the higher frequencies.
To solve this problem, we apply the pre-emphasis filter [12] in the proposed method. It enhances
the components at higher frequencies to distribute the power evenly in the frequency domain. It isapplied to ‫ݔ‬୦ୟ୰୫୭ ሺ݊ሻ to enhance the components at higher frequencies. It is calculated byᇱሺ݊ሻ ൌ ‫ݔ‬୦ୟ୰୫୭ ሺ݊ሻ െ ߙ‫ݔ‬୦ୟ୰୫୭ ሺ݊ െ ͳሻ‫ݔ‬୦ୟ୰୫୭
(8)ᇱ
where ‫ݔ‬୦ୟ୰୫୭ ሺ݊ሻ is the enhanced speech and Ƚ is the filter coefficient. The filter coefficient Ƚ isgenerally set to 0.97.
4.2 Harmonic Reconstruction after Process of the DNN
Figure 6 shows overview of the proposed method. First, the sound quality of the acquired speech is
improved by processing the DNN which is trained using a set of acoustic features extracted from otheracquired speech and the clean speech. Second, harmonic reconstruction is applied to the speechprocessed by the DNN. To avoid over-enhancement of the reconstructed harmonics at higher
frequencies, we add the processed speech to the harmonic-reconstructed speech. This process canenhance the components at frequencies which cannot be enhance by the pre-emphasis filter. In theprocess of harmonic reconstruction, the speech harmonics are reconstructed based on Section 4.1. As
shown in Eqs. (2) and (8), harmonic reconstruction in the proposed method can suppress thecomputation load.

Figure 5 – The time waveform and log-power spectrum of the acquired speech ‫ݔ‬ሺ݊ሻ, effect of the
non-linear function ‫݌‬൫‫ݔ‬ሺ݊ሻ൯ and harmonic-reconstructed speech ‫ݔ‬୦ୟ୰୫୭ ሺ݊ሻ (solid line) and
log-power spectrum of the clean speech (dotted line)

Figure 6 – Overview of the proposed method

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5. EVALUATION EXPERIMENT ABOUT THE SOUND QUALITY OF THE
IMPROVED SPEECH
5.1 Experimental Setup
We carried out an evaluation experiment to verify the effectiveness of the proposed method. In thisexperiment, we compared the sound qualities of the acquired speeches without any methods, withharmonic reconstruction, with the DNN, and with the proposed method. In this experiment, we carriedout the measurement with the laser microphone to prepare the training and test data. Figure 7 and Table
1 respectively shows the arrangement and the measurement conditions. We used 7-hour utterances(5,024 utterances = 50 speakers ൈ 2 genders ൈ 50 or 53 sentences) from ATR phoneme balanced
sentences [13] for training data and 1-hour utterances (3,888 utterances = 9 speakers ൈ 2 genders ൈ
216 words) from ATR phoneme balanced words [10] for test data.
In this experiment, the DNN used a feed-forward architecture. To verify the effectiveness of thenumber of the hidden layers, the DNN was used with 2 layers and 10 layers. We extracted log-power
spectrum and short-time waveform from the speech with 256-points frame. For the input units of the
DNN, they were used concatenated frames along with left and right of 11 frames. For the input andoutput units, the dimension of log-power spectrum was 257 and the dimension of short-time waveformwas 256. Log-power spectrum was calculated by 512-point discrete Fourier transform with zero
padding. The activation functions for the hidden units and for the output units were Rectified Linear
Units (ReLUs) [14] and the sigmoid functions, respectively. The number of the hidden units in eachlayer was 2,048.
To evaluate the sound quality of the improved speech, we used perceptual evaluation of speech
quality (PESQ) [15]. PESQ is the quality measure that correlates with the subjective evaluation andshows that the larger its value is, the higher the sound quality of speech is. The range of its value is -0.5
Ң4.5.
5.2 Experimental Results
Figure 8 shows the spectrograms of the speech acquired with the laser microphone and the acquired
speech processed by the harmonic reconstruction. As shown in Fig. 8 (a), the speech acquired with thelaser microphone includes the stationary noise and the speckle noise. As shown in Fig . 8 (b),harmonics of the processed speech are ineffectively reconstructed because of the noises. Figure 9shows the spectrograms of the speeches processed to the acquired speech by the DNN with 2 or 10
hidden layers. The speeches shown in Fig. 9 (a), (b) and Fig. 9 (c), (d) are trained using log-power
spectrum and short-time waveform, respectively. From Fig. 9, we can see that the noises are reducedby the DNN trained by each training condition. However, the noise remains in the speech processedusing short-time waveform. Figure 10 shows the spectrograms of speeches applied harmonicreconstruction to the speeches in Fig. 9. As shown in Fig. 10, the speech harmonics are effectively
reconstructed in the red squares where the noise is reduced at higher frequencies.
Table 1 – Experimental conditions for training and test data inspeech measurement with the laser microphone

Figure 7 – Arrangement for training andtest data in speech measurement with thelaser microphone

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Environment

Conference room

Ambient noise level

33.4 dB

Output signal level

90 dB

Sampling frequency

8 kHz

Training data

7-hours utterances(5,024 utterances) in ATR
phoneme balanced sentences

Test data

1-hour utterances(3,888 utterances) in ATR
phoneme balanced words

(a)
(b)
Figure 8 – Spectrograms of (a) the speech acquired with the laser microphone and(b) the acquired speech processed byy the harmonic reconstruction

(a)
(b)
(c)
(d)
Figure 9 – Spectrograms of speeches processed by the trained DNN with (a) 2 and (b) 10 hidden layers
usingg log-power
spectrum,and (c)using
gpp( ) 2 and ((d)) 10 hidden layersyg short-time waveform

(a)
(b)
(c)
(d)
Figure 10 – Spectrograms of speeches applied harmonic reconstruction
to the speeches in Figure 9

Figure 11 – PESQ scores
Figure 11 shows the evaluation result of PESQ. In Fig. 11, the horizontal axis shows the methodsof speech quality improvement by using no processing, the deep learning and the proposed method.
Also, the vertical axis shows the value of PESQ calculated from test data. Each bar shows the averagevalues and the error bar shows the standard deviation. As shown in Fig. 11, the PESQ of the proposed
method is larger than that of the deep learning when the DNN is trained using the log-power spectrum.
On the other hand, the PESQ of the proposed method is smaller than that of the deep learning when the
DNN is trained using the short-time waveform. This is because the proposed method can ineffectively
reconstruct the speech harmonics by the noise around the speech harmonics at low frequencies. To
solve this problem, we need to reconstruct the speech harmonics from the speech with this noise.
Furthermore, we need to prepare the more training data for the DNN which effectively performs to
reduce the noises.

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6. CONCLUSIONSIn this paper, we propose the method of the speech quality improvement with deep learning andharmonic reconstruction for the speech acquired with the laser microphone. In the proposed method,
the non-linear function is applied to reconstruct the speech harmonics and th e pre-emphasis filter isapplied to enhance the low power at higher frequencies. We evaluated the sound quality of the
improved speech by PESQ while changing acoustic features and the number of the hidden layers in the
DNN. As the result, the proposed method using log-power spectrum is more effective than the
proposed method using short-time waveform. For improvement of the DNN using short-time
waveform, it is necessary to reduce the noise around the speech harmonics at low frequencies. In the
future, we intend to modify the harmonic reconstruction such as adjusting the power of the harmonicreconstruction by analyzing the tendency of the attenuation and the noise. Furthermore, we will
improve the performance of the DNN by preparing the larger training set.

ACKNOWLEDGEMENTS
This work was partly supported by JST COI, JSPS KAKENHI Grant Numbers JP18K19829 andJP19H04142.

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