Improved speech presence probability estimation based on wavelet denoising

A reliable estimator for speech presence probability (SPP) can significantly improve the performance of many speech enhancement algorithms. Previous work showed that a good SPP estimator can be obtained by using a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. In this paper, a wavelet based denoising algorithm is proposed for such purpose. We first apply the wavelet transform to the periodogram of a noisy speech signal to generate an oracle for indicating the locations of the noise floor in the periodogram. We then make use of that oracle to selectively remove the wavelet coefficients of the noise floor in the log multitaper spectrum (MTS) of the noisy speech. The remaining wavelet coefficients are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables a significantly improvement in the quality and intelligibility of the enhanced speeches. © 2012 IEEE.published_or_final_versio

Realtime implementation of a particle filter with integrated voice activity detector for acoustic speaker tracking

Abstract-In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT) using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. This work focuses on a realtime implementation of the ASLT algorithm proposed in [1], which circumvents this problem by integrating measurements from a voice activity detector (VAD) within the tracking algorithm framework. The algorithm is here optimized for low computational complexity, and is implemented on a PC based real-time system. The resulting computational load is calculated and is presented along with real measurements of the true execution speed for the considered algorithm implementation. The results show that the algorithm is suitable for implementation in currently existing low-power embedded systems

Voice Activity Detection Using Deep Neural Network

13301甲第4842号博士（工学）金沢大学博士論文要旨Abstract 以下に掲載：Eurasip Journal on Audio, Speech and Music Processing 2018(1) pp.1-15 2018. Springer International Publishing. 共著者：Suci Dwijayanti, Kei Yamamori, Masato Miyosh

Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold

Traditionally voice activity detection algorithms are based on any combination of general speech properties such as temporal energy variations, periodicity, and spectrum. This paper describes a novel statistical method for voice activity detection using a signal to noise ratio measure. The method employs a low-variance spectrum estimate and determines an optimal threshold based on the estimated noise statistics. A possible implementation is presented and evaluated over a large test set and compared to current modern standardized algorithms. The evaluations indicate promising results with the proposed scheme being comparable or favorable over the whole test set

Повышение робастности систем автоматического распознавания речи методами обработки сигналов

Дисертацію присвячено вирішенню актуальної задачі підвищення робастності систем автоматичного розпізнавання мовлення шляхом розробки нових методів обробки мовленнєвих сигналів. Удосконалено метод ослаблення пізньої реверберації, що дозволяє підвищити точність систем автоматичного розпізнавання мовлення навіть в умовах недостатності апріорної інформації про параметри реверберації. Проведена експериментальна перевірка доцільності використання подання мовленнєвих сигналів в просторі ознак PNCC разом з використанням детектора голосової активності, що дозволяє забезпечити робастність системи автоматичного розпізнавання мовлення при використанні PNCC ознак в умовах нестаціонарного шуму. За отриманими результатами зроблено висновок про необхідність вдосконалення методу PNCC шляхом заміни процедури роздільної обробки голосової активності на основі енергетичного підходу на більш стійкі щодо дії нестаціонарних шумів методи. Розроблено нейромережевий детектор голосової активності системи автоматичного розпізнавання мовлення, що дало можливість використовувати такі ознаки як нормалізовані за потужністю кепстральні коефіцієнти при роботі з нестаціонарними шумами. Розширено перелік ознак запропонованого нейромережевого детектору голосової активності за рахунок введення ознаки «траєкторія основного тону», що дозволило підвищити завадостійкість його роботи. Удосконалено метод навчання нейромережевого детектора голосової активності. Для цього запропоновано алгоритм адаптивної корекції параметрів стаціонарної нелінійної MLP мережі, що дозволило прискорити процедуру навчання такого детектора. Працездатність та ефективність запропонованого детектору голосової активності була експериментально підтверджена шляхом тестування на стандартних сигналах, спотворених білим та рожевим шумами та на реальних сигналах, отриманих з телефонного каналу зв’язку NTIMIT. Результати порівняння запропонованого детектору MLP-IDBD з алгоритмами Д. Їнґ, Д. Согн та алгоритмами міжнародних стандартів ETSI AMR та ITU G.729 показали, що запропонований в даній дисертації детектор MLP-IDBD має перевагу над конкурентними аналогами за критерієм проценту правильно розпізнаних фреймів

Development of algorithms for smart hearing protection devices

In industrial environments, wearing hearing protection devices is required to protect the wearers from high noise levels and prevent hearing loss. In addition to their protection against excessive noise, hearing protectors block other types of signals, even if they are useful and convenient. Therefore, if people want to communicate and exchange information, they must remove their hearing protectors, which is not convenient, or even dangerous. To overcome the problems encountered with the traditional passive hearing protection devices, this thesis outlines the steps and the process followed for the development of signal processing algorithms for a hearing protector that allows protection against external noise and oral communication between wearers. This hearing protector is called the “smart hearing protection device”. The smart hearing protection device is a traditional hearing protector in which a miniature digital signal processor is embedded in order to process the incoming signals, in addition to a miniature microphone to pickup external signals and a miniature internal loudspeaker to transmit the processed signals to the protected ear. To enable oral communication without removing the smart hearing protectors, signal processing algorithms must be developed. Therefore, the objective of this thesis consists of developing a noise-robust voice activity detection algorithm and a noise reduction algorithm to improve the quality and intelligibility of the speech signal. The methodology followed for the development of the algorithms is divided into three steps: first, the speech detection and noise reduction algorithms must be developed, second, these algorithms need to be evaluated and validated in software, and third, they must be implemented in the digital signal processor to validate their feasibility for the intended application. During the development of the two algorithms, the following constraints must be taken into account: the hardware resources of the digital signal processor embedded in the hearing protector (memory, number of operations per second), and the real-time constraint since the algorithm processing time should not exceed a certain threshold not to generate a perceptible delay between the active and passive paths of the hearing protector or a delay between the lips movement and the speech perception. From a scientific perspective, the thesis determines the thresholds that the digital signal processor should not exceed to not generate a perceptible delay between the active and passive paths of the hearing protector. These thresholds were obtained from a subjective study, where it was found that this delay depends on different parameters: (a) the degree of attenuation of the hearing protector, (b) the duration of the signal, (c) the level of the background noise, and (d) the type of the background noise. This study showed that when the fit of the hearing protector is shallow, 20 % of participants begin to perceive a delay after 8 ms for a bell sound (transient), 16 ms for a clean speech signal and 22 ms for a speech signal corrupted by babble noise. On the other hand, when having a deep hearing rotection fit, it was found that the delay between the two paths is 18 ms for the bell signal, 26 ms for the speech signal without noise and no delay when speech is corrupted by babble noise, showing that a better attenuation allows more time for digital signal processing. Second, this work presents a new voice activity detection algorithm in which a low complexity speech characteristic has been extracted. This characteristic was calculated as the ratio between the signal’s energy in the frequency region that contains the first formant to characterize the speech signal, and the low or high frequencies to characterize the noise signals. The evaluation of this algorithm and its comparison to another benchmark algorithm has demonstrated its selectivity with a false positive rate averaged over three signal to noise ratios (SNR) (10, 5 and 0 dB) of 4.2 % and a true positive rate of 91.4 % compared to 29.9 % false positives and 79.0 % of true positives for the benchmark algorithm. Third, this work shows that the extraction of the temporal envelope of a signal to generate a nonlinear and adaptive gain function enables the reduction of the background noise, the improvement of the quality of the speech signal and the generation of the least musical noise compared to three other benchmark algorithms. The development of speech detection and noise reduction algorithms, their objective and subjective evaluations in different noise environments, and their implementations in digital signal processors enabled the validation of their efficiency and low complexity for the the smart hearing protection application