Speech Enhancement Methods

Cílem práce je objasnit některé jednokanálové metody pro zpracováni řeči. V této práci jsou rozebrané metody: základní metoda spektrálního odečítání, modifikovaná metoda spektrálního odečítání, pásmové spektrální odečítání a spektrální odečítání MMSE, Wienerovo filtrování. Všechny metody jsou implementovány. Kromě toho jsou v této práci popsané předzpracování řeči, detektor řečové aktivity a hodnocení řeči.Aim of this work is summarize some single-channel methods of speech enhancement. These methods are explained in this work: Basic Spectral Subtraction Method, Modified Spectral Subtraction, Multi-band Spectral subtraction, spectral subtraction MMSE and Wiener filtering. All methods are implemented. Preprocessing, voice activity detector and speech scores are explained in this paper, too.

Blind Normalization of Speech From Different Channels

We show how to construct a channel-independent representation of speech that has propagated through a noisy reverberant channel. This is done by blindly rescaling the cepstral time series by a non-linear function, with the form of this scale function being determined by previously encountered cepstra from that channel. The rescaled form of the time series is an invariant property of it in the following sense: it is unaffected if the time series is transformed by any time-independent invertible distortion. Because a linear channel with stationary noise and impulse response transforms cepstra in this way, the new technique can be used to remove the channel dependence of a cepstral time series. In experiments, the method achieved greater channel-independence than cepstral mean normalization, and it was comparable to the combination of cepstral mean normalization and spectral subtraction, despite the fact that no measurements of channel noise or reverberations were required (unlike spectral subtraction).Comment: 25 pages, 7 figure

Communication system with adaptive noise suppression

A signal-to-noise ratio dependent adaptive spectral subtraction process eliminates noise from noise-corrupted speech signals. The process first pre-emphasizes the frequency components of the input sound signal which contain the consonant information in human speech. Next, a signal-to-noise ratio is determined and a spectral subtraction proportion adjusted appropriately. After spectral subtraction, low amplitude signals can be squelched. A single microphone is used to obtain both the noise-corrupted speech and the average noise estimate. This is done by determining if the frame of data being sampled is a voiced or unvoiced frame. During unvoiced frames an estimate of the noise is obtained. A running average of the noise is used to approximate the expected value of the noise. Spectral subtraction may be performed on a composite noise-corrupted signal, or upon individual sub-bands of the noise-corrupted signal. Pre-averaging of the input signal's magnitude spectrum over multiple time frames may be performed to reduce musical noise

Wavelet Transformation and Spectral Subtraction Method in Performing Automated Rindik Song Transcription

Rindik is Balinese traditional music consisting of bamboo rods arranged horizontally and played by hitting the rods with a mallet-like tool called "panggul". In this study, the transcription of Rindik's music songs was carried out automatically using the Wavelet transformation method and spectral subtraction. Spectral subtraction method is used with iterative estimation and separation approaches. While the Wavelet transformation method is used by matching the segment Wavelet results with the Wavelet result references in the dataset. The results of the transcription were also synthesized again using the concatenative synthesis method. The data used is the hit of 1 Rindik rod and a combination of 2 Rindik rods that are hit simultaneously, and for testing the system, 4 Rindik songs are used. Each data was recorded 3 times. Several parameters are used for the Wavelet transformation method and spectral subtraction, which are the length of the frame for the Wavelet transformation method and the tolerance interval for frequency difference in spectral subtraction method. The test is done by measuring the accuracy of the transcription from the system within all Rindik song data. As a result, the Wavelet transformation method produces an average accuracy of 83.42% and the spectral subtraction method produces an average accuracy of 78.51% in transcription of Rindik songs

Spectral subtractive type speech enhancement methods

In this paper spectral subtractive method and some of its modification are compared. Performance of spectral subtraction, its limitations, artifacts introduced by it, and spectral subtraction modifications for eliminating these artifacts are discussed in the paper in details. The algorithms are compared based on SNR improvement introduced by them. Spectrograms of speech enhanced by the algorithms, which show the algorithms performance and degree of speech distortion, are also presented

A Novel Scheme of Speech Enhancement using Power Spectral Subtraction - Multi-Layer Perceptron Network

A novel method for eliminating noise from a noised speech signal in order to improve its quality using combined power spectral subtraction and multi-layer perceptron network is presented in this paper. Firstly, the contaminated speech signal was processed by spectral subtraction to enhance the clean speech signal. Then, the signal was processed by a neural network using the spectral subtraction parameters and result of estimated speech signal in order to improve its signal quality and intelligibility. The artificial neural network used was multi-layer perceptron network consisted of three layers with six input and one output. The neural network was trained with three speech signals contaminated with two level white gaussian noises in SNR including 0 dB and 30dB. The designed speech enhancement was examined with ten noised speech signals. Based on the experiments, the improvement of signal quality SNR was up to 7 dB when the signal quality input was 0dB. Then, based on the PESQ score, the proposed method can improve up to 0.4 from its origin value. Those experiment results show that the proposed method is capable to improve both the signal quality and intelligibility better than the original power spectral subtraction

Nonlinear Spectral Subtraction Berbasis Tsallis Statistics Untuk Peningkatan Kualitas Sinyal Ucapan

Adanya derau (noise) mengurangi kualitas dan inteligibilitas dari sinyal ucapan dan ini berakibat menurunnya performa dari aplikasi berbasis sinyal ucapan. Pengurangan spektral (spectral subtraction) adalah salah satu metode yang populer untuk menghilangkan derau tersebut. Akan tetapi, pengurangan spektral memiliki kelemahan, yaitu memperkenalkan musical noise. Telah banyak turunan dari pengurangan spektral yang diusulkan untuk mengurangi musical noise. Salah satunya adalah menggunakan oversubtraction dalam formulasi pengurangan spektral. Pendekatan ini disebut nonlinear pengurangan spektral. Akan tetapi, penentuan faktor ini secara heuristik. Dengan menggunakan Tsallis statistics, nonlinear subtraksi dapat diturunkan secara matematis. Varian baru spectral subtraction yang disebut q-spectral subtraction telah diturunkan. Metode ini telah terbukti efektif untuk meningkatkan performa sistem pengenalan ucapan terhadap noise. Akan tetapi, evaluasi metode ini untuk meningkatkan kualitas sinyal ucapan pada speech enhancement belum diinvestigasi. Pada paper ini, performa q-SS untuk speech enhancement akan diivestigasi. Dari hasil percobaan, ditemukan bahwa q-SS lebih baik dalam meningkatkan kualitas sinyal ucapan dibandingkan metode pengurangan spektral lain