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Voice activity detection algorithm based on long-term pitch information


A new voice activity detection algorithm based on long-term pitch divergence is presented. The long-term pitch divergence not only decomposes speech signals with a bionic decomposition but also makes full use of long-term information. It is more discriminative comparing with other feature sets, such as long-term spectral divergence. Experimental results show that among six analyzed algorithms, the proposed algorithm is the best one with the highest non-speech hit rate and a reasonably high speech hit rate.

1 Introduction

Voice activity detection (VAD) is an essential module in almost every audio signal processing application, including coding, enhancement, and recognition. VAD can increase efficiency and improve recognition rates by removing insignificant parts from the audio signals, such as silences or background noises and retaining human voices. In high signal-to-noise ratio (SNR) conditions, it is a relatively simple task since we can reach a satisfying result only by computing the frame energies and setting an appropriate threshold for classification [1]. However, in modern real-life applications, audio signals are always corrupted by the background noises which make those simple VAD algorithms deteriorate dramatically.

For VAD under extreme noisy conditions, a considerable amount of research has been done [25]. And the main difference of these algorithms lies in the exploited feature sets in the systems, including spectrum-based features [6], cepstrum-based features [7], fundamental frequency-based features [8], entropy [9], harmonic [10], and energy-based features. Among these features, long-term spectral divergence (LTSD) feature [11] stands out because of its simplicity, adaptability, and good behaviors. Nevertheless, the performance may still need to be improved in non-stationary noises, especially in environmental noises such as factory or battlefield noises which are usually characterized by large, irregular random bursts embedded in a relatively stationary background [12].

In this paper, we propose a new VAD algorithm based on long-term pitch divergence (LTPD) features. Different from the LTSD feature, LTPD takes advantage of time-varying pitch information [13] and can deal with the tough noises mentioned above. In a sense, the pitch is a special type of spectrum. Both of them try to decompose audio signals into spectral bands; however, the scale of pitch bands is not linear but in some logarithmic fashion, that is termed as the equal-tempered scale. In musically related task, this logarithmic form of decomposition has been proved to be more suitable for human perception and pitch-based features are more discriminative. Thus, compared to LTSD, LTPD not only benefits from the long-term information about speech signals but also benefits from the logarithmic decomposition of speech signals which is more reasonable than spectrum. The experimental results show that the average performance of the proposed method is the best among the VADs analyzed.

The outline of this paper is as follows: Pitch-based audio features are given in Section 2. Then, we present our LTPE-VAD algorithm in Section 3. Section 4 depicts database and experimental setup and analyzes evaluation results. Finally, the conclusion is given in Section 5.

2 Long-term pitch divergence features

2.1 Pitch-based audio features

The equal-tempered frequency scale used in Western classical music is not linear, but logarithmic due to the facts that humans perceive musical intervals approximately logarithmically. Let f(p) denote the center frequency of the pitch p [21 : 108] corresponding to the musical note A0 to C8. And the pitch 108 is corresponding to frequency 4186 Hz. Then, the relationship between the pitch p and its center frequency f(p) is given by:

$$ f(p)={2}^{\frac{p-69}{12}}\cdot 440 $$

Pitch-based audio features are extracted by decomposing audio signals into 88 frequency bands, where each band corresponds to a pitch of the equal-tempered scale [13]. The decomposition is realized by a suitable multi-rate filter bank consisting of elliptic filters [13]. This representation of audio signals can then be used as a basis for deriving various audio features of various characteristics [13, 14], such as chroma pitch, chroma log pitch, and chroma energy normalized statics [15].

Figure 1 shows the waveform of an audio recording and its corresponding pitch features in various noise environments. It can be seen that the energies of this audio are mostly concentrated in pitches range from pitch 57 to pitch 102. And other pitches are easily corrupted by noise. Theoretically, it has a weak effect on speech intelligibility when filtering out the low frequency parts of speech [16] which are easy to be distorted by noises. The low frequency endpoint is commonly 300 or 500 Hz, but no lower than 220 Hz [17] corresponding to pitch 57. While the most critical intelligibility elements of speech lie above 3 kHz, the most of average energies in speech signals lie below 3 kHz [17] which are of more importance for speech/non-speech detection. Thus, pitches range from 57 to 102 are used in the proposed method.

Fig. 1
figure 1

Waveform and pitch feature representation for an audio recording

2.2 Definition of LTPD

Let X be a sequence of pitch features and, X(p, t) be the value of pth pitch at frame t, where p = 57, , 102 and t = 1, 2, , T. The M-order long-term pitch envelope (LTPE) is defined as follows:

$$ {\mathrm{LTPE}}_M\left(p,t\right)= \max \left\{X\left(p,t-M+m\right)\left|m=0,1,\cdots, 2M\right.\right\} $$

The noise pitch features N is estimated from X by using the MMSE-based estimator [18]. And the average noise pitch \( \overline{N}(p) \) for the pth pitch band at frame t is defined as:

$$ {\overline{N}}_t(p)=\frac{1}{t}\left(\left(t-1\right){\overline{N}}_{t-1}(p)+N\left(p,t\right)\right),t=2,\cdots, T $$

where, N(p, t) is the noise feature value of pth pitch at frame t and \( {\overline{N}}_1(p)=N\left(p,1\right) \).

The M-order long-term pitch divergence between speech and noise is defined as the deviation of the LTPE respect to the pth average noise pitch and is given by:

$$ {\mathrm{LTPD}}_M(t)=10{ \log}_{10}\left(\frac{1}{46}{\displaystyle \sum_{p=57}^{102}\frac{{\mathrm{LTPE}}_M^2\left(p,t\right)}{{\overline{N}}_t^2(p)}}\right) $$

The definitions are quite identical between LTPD and LTSD. The main difference is the scale of spectral bands, logarithmic rather than linear. However, this subtlety is of considerable importance because logarithmic spectral decomposition is superior to the linear form in theory as well as in practice. And this conclusion can be proved by comparing the distributions of LTPD and LTSD shown in Section 2.3.

2.3 LTPD distributions of speech and non-speech

In this section, we will present the distributions of the LTPD as a function of the window order M so as to clarify the motivations for the algorithm proposed. To study the distribution of the LTPD feature, speeches from the TIMIT corpus [19] and noises (factory, fighter jet, destroyer, and tank noise) from the NOISEX-92 corpus [20] were used in the analyses. More details about the databases will be presented in Section 4.

Figures 2 and 3 show the effects of window length on distributions of LTPD and LTSD for speech and non-speech, respectively. Figure 4 shows the speech, non-speech, and total detection errors vs. the window length. Comparing Fig. 2 with Fig. 3, it can be concluded that the LTPD feature is more discriminative than LTSD feature. In Fig. 2, it is not difficult to find out that the distributions of speech and non-speech are more easily separated along with the increasing window length M. In corroboration, the speech classification error is reduced when increasing the order of the long-term window, as shown in Fig. 4. The optimal value of the order of window would be M = 3 according to the total misclassification errors of speech and noise in Fig. 4.

Fig. 2
figure 2

Effect of window length on the LTPD distribution (SNR = −5 dB): speech (red full line) and non-speech (blue dashed line)

Fig. 3
figure 3

Effect of window length on the LTSD distribution (SNR = −5 dB): speech (red full line) and non-speech (blue dashed line)

Fig. 4
figure 4

Speech, non-speech, and total detection errors vs. the window length (SNR = −5 dB)

The conclusion about LTPD above is identical to that the conclusion in [11] concerning the effect of window length on LTSD feature. Consequently, LTPD can also take advantage of the long-time information of speech as LTSD does.

3 The proposed VAD algorithm

A flowchart diagram of LTPD-based VAD algorithm is shown in Fig. 5. The specific procedure can be described as follows. In the initialization step, the MMSE-based noise estimator is initialized by using the first N frames and the pitch filter banks are designed (see [13] for details). After initialization, the pitch features are extracted by applying the pitch filter banks to audio signals. Then, the LTPE is estimated by means of Eq. (2), the average noise pitch feature is obtained by using Eq. (3), and the LTPD is computed as Eq. (4). The original VAD decisions are made by comparing the LTPD value of each frame to a given threshold γ. If the LTPD value is larger than the threshold, the current frame is labeled as speech; otherwise, it is labeled as silence. The final VAD decisions are obtained from original decisions by applying the hang-over scheme.

Fig. 5
figure 5

Flowchart diagram of LTPD-VAD algorithm

It should be noted that the distribution of LTPD changes with SNRs, thus the threshold should also vary accordingly. In LTSD-VAD algorithm, the threshold is set according to the observed noise energy levels. Here, we use a SNR-based method to determine the threshold [11]:

$$ \gamma =\left\{\begin{array}{l}{\gamma}_0\kern18.9em \mathrm{S}\mathrm{N}\mathrm{R}(t)\le {\mathrm{SNR}}_0\\ {}\frac{\mathrm{SNR}(t)-{\mathrm{SNR}}_0}{{\mathrm{SNR}}_0-{\mathrm{SNR}}_1}\left({\gamma}_0-{\gamma}_1\right)+{\gamma}_0\kern1.5em {\mathrm{SNR}}_0<\mathrm{S}\mathrm{N}\mathrm{R}(t)<{\mathrm{SNR}}_1\\ {}{\gamma}_1\kern19.4em \mathrm{S}\mathrm{N}\mathrm{R}(t)\ge {\mathrm{SNR}}_1\end{array}\right. $$

where, SNR(t) is the SNR estimated at frame t. SNR0 and SNR1 are the SNRs in the cleanest and noisiest background noises, and γ 0 and γ 1 are their optimal thresholds, respectively.

This method is the very similar to [11]. However, since we use an MMSE-based noise estimator, the estimation of SNR is easier:

$$ \mathrm{S}\mathrm{N}\mathrm{R}(t)=10{ \log}_{10}\left(\frac{{\displaystyle {\sum}_{\tau =t-K}^t{\displaystyle {\sum}_p{X}^2\left(p,\tau \right)}}}{{\displaystyle {\sum}_{\tau =t-K}^t{\displaystyle {\sum}_p{N}^2\left(p,\tau \right)}}}-1\right) $$

where, K is a constant. The estimation of SNR is only based on the K + 1 frames before frame t; thus, it can diminish the effect of time-variation of SNR.

4 Experiments and results

To illustrate the effectiveness of LTPD-VAD, some up-to-date voice-active detection methods, which have been proved to be noise robust, are chosen for comparison. They are Sohn [21], Harmfreq [10], LTSD [11], LTSV [2], and LSFM [22].

4.1 Data and experimental setup

To evaluate the proposed method, utterances from TIMIT corpus are used. Utterances in TIMIT are on average no longer than 4 s and contain a very small number of non-speech segments. Thus, single utterance is too short to evaluate a VAD algorithm properly. Hence a number of randomly chosen utterances from every dialects (i.e., DR1 to DR8) have been concatenated into a single speech recording, adding 2.5 s of silence at the beginning, ending, and junctions of the utterances. And amplitudes of each utterance have been normalized in order to equalize the power. The initial labels have been obtained by a simple energy VAD and examined visually. 47.06 % of the whole samples are labeled as active speech samples. Two datasets, development and test, have been constructed, and the duration of each dataset is about 600 s. The development and test datasets are used to estimate the parameters and evaluate the performance, respectively.

All noise types are taking from NOISEX-92 corpus. And the complete list of noise types used in this evaluation is:

  • factory1 (noise near plate-cutting and electrical welding equipment);

  • factory2 (noise in a car production hall);

  • leopard (military vehicle noise);

  • m109 (tank noise);

  • opsroom (destroyer operations room background noise);

  • f16 (F-16 cockpit noise);

  • buccaneer1 (Buccaneer jet traveling at 190 knots)

  • buccaneer2 (Buccaneer jet traveling at 450 knots)

  • babble (100 people speaking in a canteen)

  • engine (destroyer Engine Room noise)

  • hfchannel (noise in an HF radio channel after demodulation)

  • machinegun (a .50-caliber gun fired repeatedly)

  • pink (pink noise)

  • volvo (Volvo 340 noise)

  • white (white noise)

Among these noises, only white and pink noises are stationary.

To add noises to speeches at a desired SNR, the open-source Filtering and Noise Adding Tool (FaNT)Footnote 1 is used.

The audio signals have been divided into 50 ms-long non-overlapping frames and windowed with a periodic Hamming window. The pitch features are extracted by using The Chroma Toolbox.Footnote 2 The MMSE-based noise estimator is based on MATLAB implementation estnoiseg in Voicebox.Footnote 3 The order of LTSD is 3. And to compute LTSV and LSFM, the long-term window length is 6 and the parameter of Welch-Bartlett method is 2. All of these parameter values are smaller than those recommended in the corresponding references because of a longer frame length and a lack of the overlap factor.

The receiver operating characteristic (ROC) curves and area under curve (AUC) values are used to describe the average performance of the VAD algorithms. And detection performances under different SNR levels are also assessed in terms of non-speech hit rate (HR0) and speech hit rate (HR1).

4.2 Evaluation results

Table 1 shows the AUC values of six evaluated methods for all 15 types of noises under −5 dB SNR, and the best values among all methods in different noises are given with red bold. And Fig. 6 presents the ROC curves of the evaluated algorithms for the six typical types of noise under −5 dB SNR. It can be seen that the LTPD-based VAD algorithm outperforms all other VAD methods in seven noisy cases since these noisy cases are the most non-stationary among NOISEX-92 dataset according to the variability of short-time energy. The proposed method is very suitable for such cases while other methods deteriorate dramatically. Especially for factory1 and machinegun noises, the proposed method still obtains good results while some other VAD methods seem to exhibit worse performance nearly close to random guess. This is due to the fact that both machinegun and factory1 noises consist of mainly two different signals: gun firing and silence between firing for machinegun noise [2], and relatively stationary background noise of electric motor roaring as well as embedded irregular random bursts like metal banging or plate-cutting for factory1 noise, leading to misclassifications between noisy speech and noises because of the similar non-stationary degrees. Moreover, comparing with the silence background in machinegun noise, the background noise of factory1 is more complex and challenging, resulting in higher misclassification errors.

Table 1 AUC values of the evaluated VAD algorithms under −5 dB SNR
Fig. 6
figure 6

ROC curves of the evaluated VAD algorithms under −5 dB SNR

For other noises such as m109, opsroom, engine, and hfchannel, the best performance is obtained by the LTSV-based VAD algorithm, which means the LTSV measure can effectively distinguish these noises from the corresponding noisy speech. Not only does LTSV method takes advantage of the long-term information but also benefits from the signal variability defined in LTSV. However, the LTPD-based VAD algorithm still outperforms other algorithms except LTSV.

For the vehicle interior noise like leopard and volvo, the characteristics of noisy speech do not change significantly compared to that of pure speech [2] resulting wonderful performances for all evaluated methods. As an exceptional case, all methods do not perform very well under babble noise composed of voices from 100 people speaking. However, in this case, LTSD-based VAD algorithm is superior to other algorithms, which means that linear spectrum-based LTSD measure is successful in distinguishing such noise consisting of human voices from the corresponding noisy speech.

According to the average AUC value in measuring the comprehensive property of each VAD algorithm under different noisy environments, LPTD-based VAD algorithm is significantly superior to other algorithms, even with a stronger robustness even at low SNR.

Figure 7 provides the comparisons of six evaluated VAD algorithms in terms of speech hit rate and non-speech hit rate for different SNR levels ranging from 20 to −5 dB. Note that the results show here are averaged values for the whole set of noises. It can be concluded that:

  1. 1)

    Sohn-VAD algorithm yields a moderate behavior with relatively high speech hit rate but slightly low non-speech hit rate.

  2. 2)

    Harmfreq-VAD, LTSV-VAD, and LSFM-VAD algorithms also obtain a moderate behavior with relatively high non-speech hit rate but slightly low speech hit rate.

  3. 3)

    The LTSD-VAD algorithm yields the best speech hit rate while non-speech hit rate is poor.

  4. 4)

    The LTPD-VAD achieves the best compromise among the four evaluated VADs. The speech hit rate of LTPD-VAD is less than all the other methods in clean conditions (above 5 dB) but better than Harmfreq, LTSV, and LSFM in noisy conditions (−5 dB). Moreover, its non-speech hit rate is much better than all the other methods in all cases.

Fig. 7
figure 7

Speech/non-speech hit rates of the evaluated algorithms under different SNR levels

Table 2 compares the LTPD-VAD with the other VAD methods in terms of the average speech/non-speech hit rates. LTPD-VAD yields an 87.77 % HR0 average value which is 27.97, 13.07, and 48.85 % higher than that of Sohn, Harmfreq, and LTSD-VAD methods, respectively. And LTPD-VAD attains a 94.23 % average speech hit rate while Sohn, Harmfreq, and LTSD-VAD provide 96.25, 90.63, and 98.28 %, respectively. Thus, considering speech and non-speech hit rates together, LTPD-VAD is more superior to the other VAD algorithms.

Table 2 Average speech and non-speech hit rates for SNR levels ranging from 20 to −5 dB

5 Conclusions

In this paper, a new VAD algorithm is presented for improving the performance of speech detection robustness in various noisy environments. The algorithm is based on the estimation of long-term pitch envelope and measure of long-term pitch divergence between speeches and noises. And an adapted LTPD decision threshold is also given using the measured signal-to-noise ratios. The experimental results show that the proposed method outperforms the other up-to-date VAD algorithms under the most non-stationary noisy environments and is more robust than other VAD algorithms even at low SNR due to the highest non-speech hit rate and a moderate speech hit rate.

However, from the experimental results, it can be argued that LTSV-based VAD method is superior to LTPD-based algorithm in some noisy environments (m109, opsroom, engine, and hfchannel). This may indicate that the long-term signal variability based on logarithmic spectrum decomposition, constructed by combining pitch feature with LTSV feature, may be suitable for VAD tasks. Further, comparing with strict logarithmic scale, some critical-band-based scales is more conforming to human perception of speech signals. Hence, studies of combining these critical-band-based spectrum decomposition with long-term spectral divergence or long-term signal variability are worth further exploration.






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This work was supported in part by the National Natural Science Foundation of China (No. 61175017, No. 61370034, and No. 61403224).

The pitch is different to that used in speech signal processing. Here, the pitches mean the spectral bands corresponding to the equal-tempered scale as used in Western music.

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The authors declare that they have no competing interests.

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Correspondence to Dan Qu.

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Yang, XK., He, L., Qu, D. et al. Voice activity detection algorithm based on long-term pitch information. J AUDIO SPEECH MUSIC PROC. 2016, 14 (2016).

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