Voice Activity Detection Scheme Research Articles

Robust voice activity detection based on weighted average of long-term quadratic Renyi and differential entropies

We propose two novel voice activity detection (VAD) algorithms by employing i) the Rényi entropy estimate, and ii) a weighted combination of Rényi and differential entropies, at each frequency bin of power spectral estimates, by considering overlapping frames in the received signal samples. The Bartlett-Welch method is employed to estimate the power spectrum from a suitably chosen number of past frames in the frequency domain. Exploiting the known fact that a long-term averaging enhances the performance of VAD, we first propose a novel frequency domain long-term quadratic Rényi entropy (FLQRE) feature, which is extracted by summing the estimated Rényi entropy values across the frequency bins over each signal frame. Later, we propose a weighted combination of the frequency domain long-term differential entropy (FLDE) and FLQRE feature for VAD, termed as a weighted quadratic and differential entropy (WQDE) feature. We evaluate the performance of the proposed FLQRE and WQDE VAD schemes, by considering twelve types of noises from NOISEX-92 at five different SNR values, artificially added to speech samples from SWITCHBOARD and TIMIT corpora. We present an extensive performance comparison study to establish the utility of our proposed VAD algorithms over state-of-the-art short- and long-term VAD techniques such as ITU-T G.729B, likelihood ratio test, long-term signal variability, and long-term spectral flatness measure-based algorithms. We show that the proposed algorithms yield the best average accuracy and noise-hit rate under the SWITCHBOARD corpus, and yield a comparable performance under the TIMIT corpus.

Robust Audio Content Classification Using Hybrid-Based SMD and Entropy-Based VAD.

A robust approach for the application of audio content classification (ACC) is proposed in this paper, especially in variable noise-level conditions. We know that speech, music, and background noise (also called silence) are usually mixed in the noisy audio signal. Based on the findings, we propose a hierarchical ACC approach consisting of three parts: voice activity detection (VAD), speech/music discrimination (SMD), and post-processing. First, entropy-based VAD is successfully used to segment input signal into noisy audio and noise even if variable-noise level is happening. The determinations of one-dimensional (1D)-subband energy information (1D-SEI) and 2D-textural image information (2D-TII) are then formed as a hybrid feature set. The hybrid-based SMD is achieved because the hybrid feature set is input into the classification of the support vector machine (SVM). Finally, a rule-based post-processing of segments is utilized to smoothly determine the output of the ACC system. The noisy audio is successfully classified into noise, speech, and music. Experimental results show that the hierarchical ACC system using hybrid feature-based SMD and entropy-based VAD is successfully evaluated against three available datasets and is comparable with existing methods even in a variable noise-level environment. In addition, our test results with the VAD scheme and hybrid features also shows that the proposed architecture increases the performance of audio content discrimination.

Dual Microphone Voice Activity Detection Based on Reliable Spatial Cues.

Two main spatial cues that can be exploited for dual microphone voice activity detection (VAD) are the interchannel time difference (ITD) and the interchannel level difference (ILD). While both ITD and ILD provide information on the location of audio sources, they may be impaired in different manners by background noises and reverberation and therefore can have complementary information. Conventional approaches utilize the statistics from all frequencies with fixed weight, although the information from some time–frequency bins may degrade the performance of VAD. In this letter, we propose a dual microphone VAD scheme based on the spatial cues in reliable frequency bins only, considering the sparsity of the speech signal in the time–frequency domain. The reliability of each time–frequency bin is determined by three conditions on signal energy, ILD, and ITD. ITD-based and ILD-based VADs and statistics are evaluated using the information from selected frequency bins and then combined to produce the final VAD results. Experimental results show that the proposed frequency selective approach enhances the performances of VAD in realistic environments.

Robust Whisper Activity Detection Using Long-Term Log Energy Variation of Sub-Band Signal

The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to-noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD.

Multi Channel Voice Active Detection Using Instance Filed Auto-Interrelation Function

This research work presents a method of Voice Activity Detection (VAD) in a multi-channel communication for vigorous noise reduction using the Noise Strong Voice Speech Discovery system. This method is based on the amalgamation of two phases. The initial phase utilizes the utmost apex of the normalized Instance Filed Auto-Interrelation function in multi channel communication. The second phase uses a strange mixing of cross- interrelation and zero-transit rate of the standardize auto-interrelation to the suitable measure of signal pitch and periodicity for strong noise reduction. Existing VAD coding techniques in multi channel communication are not appropriate to specific location and disgrace the speech quality while decoding. The score outcomes of the two phases are combined using the TotalWeigh Fusion to create the proposed Auto-Interrelation Zero Transit Rate (AIZT) Voice Active Detection. Accuracy of the AIZT is compared with the standardized LTSV-based VAD scheme and VAD methods to outperform the best accomplishing method. Performance of the Auto-Interrelation Zero Transit Rate (AIZT) Voice Active Detection is measured in terms of multi channel communication efficiency, noise level. Experiments are conducted in the M2VTS audio-visual database, TIMIT dataset and the Dutch subset of a test database to evaluate the communication in multiple channels.

Efficient voice activity detection algorithm using long-term spectral flatness measure

This paper proposes a novel and robust voice activity detection (VAD) algorithm utilizing long-term spectral flatness measure (LSFM) which is capable of working at 10 dB and lower signal-to-noise ratios(SNRs). This new LSFM-based VAD improves speech detection robustness in various noisy environments by employing a low-variance spectrum estimate and an adaptive threshold. The discriminative power of the new LSFM feature is shown by conducting an analysis of the speech/non-speech LSFM distributions. The proposed algorithm was evaluated under 12 types of noises (11 from NOISEX-92 and speech-shaped noise) and five types of SNR in core TIMIT test corpus. Comparisons with three modern standardized algorithms (ETSI adaptive multi-rate (AMR) options AMR1 and AMR2 and ITU-T G.729) demonstrate that our proposed LSFM-based VAD scheme achieved the best average accuracy rate. A long-term signal variability (LTSV)-based VAD scheme is also compared with our proposed method. The results show that our proposed algorithm outperforms the LTSV-based VAD scheme for most of the noises considered including difficult noises like machine gun noise and speech babble noise.

Voice activity detection based on conjugate subspace matching pursuit and likelihood ratio test

Most of voice activity detection (VAD) schemes are operated in the discrete Fourier transform (DFT) domain by classifying each sound frame into speech or noise based on the DFT coefficients. These coefficients are used as features in VAD, and thus the robustness of these features has an important effect on the performance of VAD scheme. However, some shortcomings of modeling a signal in the DFT domain can easily degrade the performance of a VAD in a noise environment. Instead of using the DFT coefficients in VAD, this article presents a novel approach by using the complex coefficients derived from complex exponential atomic decomposition of a signal. With the goodness-of-fit test, we show that those coefficients are suitable to be modeled by a Gaussian probability distribution. A statistical model is employed to derive the decision rule from the likelihood ratio test. According to the experimental results, the proposed VAD method shows better performance than the VAD based on the DFT coefficients in various noise environments.

Voice activity detection method based on inter-frame correlation

To enhance the detection performance of statistical model-based Voice Activity Detection(VAD) using likelihood ratio test,an improved VAD was proposed by utilizing the correlation between tandem speech frames.First a priori Signal-to-Noise Ratio(SNR) was estimated using recursive estimation method based on the result of the previous speech frame instead of the traditional decision-directed method.Secondly double thresholds were designed by depending on the previous frame's detention result.Finally a detection rule was presented based on two-state Hidden Markov Model(HMM) coupled with double thresholds.The experimental results show that the inter-frame correlation based VAD scheme gets better performance than the Sohn's VAD.

Robust Voice Activity Detection Using Long-Term Signal Variability

We propose a novel long-term signal variability (LTSV) measure, which describes the degree of nonstationarity of the signal. We analyze the LTSV measure both analytically and empirically for speech and various stationary and nonstationary noises. Based on the analysis, we find that the LTSV measure can be used to discriminate noise from noisy speech signal and, hence, can be used as a potential feature for voice activity detection (VAD). We describe an LTSV-based VAD scheme and evaluate its performance under eleven types of noises and five types of signal-to-noise ratio (SNR) conditions. Comparison with standard VAD schemes demonstrates that the accuracy of the LTSV-based VAD scheme averaged over all noises and all SNRs is ~6% (absolute) better than that obtained by the best among the considered VAD schemes, namely AMR-VAD2. We also find that, at -10 dB SNR, the accuracies of VAD obtained by the proposed LTSV-based scheme and the best considered VAD scheme are 88.49% and 79.30%, respectively. This improvement in the VAD accuracy indicates the robustness of the LTSV feature for VAD at low SNR condition for most of the noises considered.

An Acoustic Source Localization and Tracking Framework Using Particle Filtering and Information Theory

The problem of detecting the location of an active acoustic source in an enclosure remains subject to a series of difficulties. Algorithms operate repeatedly on small frames of data from microphone recordings and provide estimates of the current source location. In a typical room, the quality of these recordings is affected by noise and reverberation. Additionally, the presence of silence gaps in speech signals and possible competing speakers can reduce tracking accuracy further. We discuss a novel localization and tracking framework that is based on particle filtering. This is driven by detection methods based on information theory that remain robust under reverberant and noisy environments. Integrating a second particle filter allows the system to track interchanging acoustic sources that reside far apart. A further extension involves the integration of a voice activity detection scheme that uses the same detection measures and deals with human-speech gaps. Performance is first examined using simulations that parameterize results according to environmental variables like reverberation and system geometry. The system is then used in a real-world scenario with data from multi-person meetings. Results indicate that the proposed framework outperforms all systems used for comparison in this work while remaining adequately robust in the examined environments.

Voice Activity Detection under Rayleigh distribution

This paper presents an improved Voice Activity Detection (VAD) algorithm which uses the Signal-to-Noise Ratio (SNR) measure. We assume that noise Power Spectral Density (PSD) in each spectral bin follows a Rayleigh distribution. Rayleigh distributions with its asymmetric tail characteristics give a better description of the noise PSD distribution than Gaussian distribution. Under this assumption, a new threshold updating expression is derived. Since the analytical integral of the false alarm probability, the threshold updating expression can be represented without the inverse complementary error function and low computational complexity is achieved in our system. Experimental results show that the proposed VAD outperforms or at least is comparable with the VAD scheme presented by Davis under several noise environments and has a lower computational complexity.

Speech Waveform Compression Using Robust Adaptive Voice Activity Detection for Nonstationary Noise

The voice activity detection (VAD) is crucial in all kinds of speech applications. However, almost all existing VAD algorithms suffer from the nonstationarity of both speech and noise. To combat this difficulty, we propose a new voice activity detector, which is based on the Mel-energy features and an adaptive threshold related to the signal-to-noise ratio (SNR) estimates. In this paper, we first justify the robustness of the Bayes classifier using the Mel-energy features over that using the Fourier spectral features in various noise environments. Then, we design an algorithm using the dynamic Mel-energy estimator and the adaptive threshold, which depends on the SNR estimates. In addition, a realignment scheme is incorporated to correct the sparse-and-spurious noise estimates. Numerous simulations are carried out to evaluate the performance of our proposed VAD method and the comparisons are made with a couple of existing representative schemes, namely, the VAD using the likelihood ratio test with Fourier spectral energy features and that based on the enhanced time-frequency parameters. Three types of noises, namely, white noise (stationary), babble noise (nonstationary), and vehicular noise (nonstationary) were artificially added by the computer for our experiments. As a result, our proposed VAD algorithm significantly outperforms other existing methods as illustrated by the corresponding receiver operating characteristics (ROC) curves. Finally, we demonstrate one of the major applications, namely, speech waveform compression associated with our new robust VAD scheme and quantify the effectiveness in terms of compression efficiency.

An improved voice activity detection using higher order statistics

In this paper, by using the properties of the higher order statistics (HOS) of speech and noise signals, we develop an improved voice activity detection (VAD) scheme. The proposed scheme employs the logarithm of the kurtosis of the LPC residual of a speech signal and is shown to be more effective and efficient in detecting active speech in medium to low signal-to-noise ratio (SNR) conditions without being unduly affected by the variations in the signal energy. To overcome the inability of the HOS in detecting unvoiced speech, another metric (the low band to full band energy ratio) is introduced. Depending on the estimated mean SNR, the proposed scheme works adaptively in two modes: a simple mode using only the SNR, and an enhanced mode using the HOS, the low band to full band energy ratio and the SNR. This scheme is capable of avoiding unnecessary computations, while maintaining the same performance as that working only in the enhanced mode. Simulations results are presented to demonstrate the effectiveness of the proposed voice activity detection scheme.

Voice activity detection system and method

An improved voice activity detection system and method is provided for use in speakerphones and other voice activated systems. To facilitate switching between various operating modes, the voice activity detection scheme utilizes a new voice energy term which is based on an integral of the absolute value of a derivative of a speech signal. Voice activity is detected during a silence mode by comparing a first ratio of a current voice energy value to a background noise value with a voice activity threshold value. Voice activity is detected when the first ratio is greater than the voice activity threshold value. Another step involves identifying a direction of the voice activity during a transmit and receive mode by comparing a second ratio of a transmit path voice energy value to a receive path voice energy value with a transmit threshold value and a receive threshold value. When the second ratio is greater than the transmit threshold value, voice activity is present in the transmit path. Similarly, when the second ratio is less than the receive threshold value, then voice activity is present in the receive path. Following the detection of voice activity in one of the paths, the speakerphone or voice activated system begins transitioning to the applicable mode by gradually suppressing the signal in the other path according to the value of the second ratio.