Single-Mixture Audio Source Separation by Subspace Decomposition of Hilbert Spectrum

Molla, Md. Khademul Islam; Hirose, Keikichi

doi:10.1109/tasl.2006.885254

Cited by 70 publications

(30 citation statements)

References 9 publications

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“…The EMD based approach is widely used in audio signal processing [12,13], computational neuroscience [14], climate signal analysis [15], image processing [16], seismic signal [17] and biomedical signal processing [18]. This study focuses on the application of EMD in advanced speech signal processing including fundamental frequency estimation [19], voiced/unvoiced speech classification [20] and speech enhancement [21,22]. The remaining parts of the paper are organized as -the basics of EMD and its limitations are described in Section 2, the EMD based approaches to speech signal processing and enhancement are explained in Section 3 and Section 4 respectively, some experimental results and discussion are illustrated in Section 5 and Section 6 includes the concluding remarks.…”

Section: Empirical Mode Decomposition (Emd) Is Newlymentioning

confidence: 99%

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Empirical Mode Decomposition for Advanced Speech Signal Processing

Molla

Das

Hamid

et al. 2013

Journal of Signal Processing

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Empirical mode decomposition (EMD) is a newly developed tool to analyze nonlinear and non-stationary signals.It is used to decompose any signal into a finite number of time varying subband signals termed as intrinsic mode functions (IMFs). Such data adaptive decomposition is recently used in speech enhancement. This study presents the concept of EMD and its application to advanced speech signal processing paradigms including speech enhancement by soft-thresholding, voiced/unvoiced (V/Uv) speech discrimination and pitch estimation. The speech processing is frequently performed in the transformed domain and the transformation is usually achieved by traditional signal analysis techniques i.e. Fourier and wavelet transformations. These analysis methods employ priori basis function and it is not suitable for data adaptive analysis for non-stationary signal like speech. Recently, EMD is taken much attention for speech signal processing in data adaptive way. Several EMD based potential soft-thresholding algorithms for speech enhancement are discussed here. The V/Uv discrimination is an important concern in speech processing. It is usually performed by using acoustic features. The training data is used to determine the threshold for classification. The EMD based data adaptive thresholding approach is developed for V/Uv discrimination without any training phase. Noticeable improvement is achieved with the application of EMD in pitch estimation of noisy speech signals. The related experimental results are also presented to realize the effectiveness of EMD in advanced speech processing algorithms.

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Section: Empirical Mode Decomposition (Emd) Is Newlymentioning

confidence: 99%

“…Although there are numerous methods [22] of speech enhancement, this study focuses on soft-thresholding based approach. The softthresholding is the way to suppress the noise effects from the noisy speech signal by comparing the analyzing signal with a predefined threshold value.…”

Section: Speech Enhancementmentioning

confidence: 99%

Empirical Mode Decomposition for Advanced Speech Signal Processing

Molla

Das

Hamid

et al. 2013

Journal of Signal Processing

Self Cite

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“…Secondly, the subspace technique based unsupervised SCSS methods using NMF [5,6] or independent subspace analysis (ISA) [7] which usually factorizes the spectrogram of the input signal into elementary components. Of special interest, EMD [8] based unsupervised SCSS methods which can separate audio mixed signal in time domain and recover sources by combing other data analysis tools, e.g. independent component analysis (ICA) [9] or principle component analysis (PCA).…”

Section: Single Channel Source Separation (Scss)mentioning

confidence: 99%

Non-Negative Matrix Factorization with Sparsity Learning for Single Channel Audio Source Separation

Gao¹,

Woo²

2012

Independent Component Analysis for Audio and Biosignal Applications

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“…Computational Auditory Scene Analysis (CASA) is one of the first methods that tried to decrypt the human auditory system in order to perform an automatic audio source separation system [6]. A recent advancement of single mixture audio separation is the independent subspace analysis (ISA) method [7,8]. The study [8] describes a single stage source separation using EMD and ICA.…”

Section: Introductionmentioning

confidence: 99%

“…A recent advancement of single mixture audio separation is the independent subspace analysis (ISA) method [7,8]. The study [8] describes a single stage source separation using EMD and ICA. The method proposed KLD based clustering algorithm to group the independent basis vectors and experimental results show a good source separation performance.…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement Using Hilbert Spectrum and Wavelet Packet Based Soft-Thresholding

Hamid¹,

Molla

Khan³

et al. 2015

CSSP

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Abstract:A method of and a system for speech enhancement consists of Hilbert spectrum and wavelet packet analysis is studied. We implement ISA to separate speech and interfering signals from single mixture and wavelet packet based softthresholding algorithm to enhance the quality of target speech. The mixed signal is projected onto time-frequency (TF) space using empirical mode decomposition (EMD) based Hilbert spectrum (HS). Then a finite set of independent basis vectors are derived from the TF space by applying principal component analysis (PCA) and independent component analysis (ICA) sequentially. The vectors are clustered using hierarchical clustering to represent the independent subspaces corresponding to the component sources in the mixture. However, the speech quality of the separation algorithm is not enough and contains some residual noises. Therefore, in the next stage, the target speech is enhanced using wavelet packet decomposition (WPD) method where the speech activity is monitored by updating noise or unwanted signals statistics. The mode mixing issue of traditional EMD is addressed and resolved using ensemble EMD. The proposed algorithm is also tested using short-time Fourier transform (STFT) based spectrogram method. The simulation results show a noticeable performance in the field of audio source separation and speech enhancement.

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Single-Mixture Audio Source Separation by Subspace Decomposition of Hilbert Spectrum

Cited by 70 publications

References 9 publications

Empirical Mode Decomposition for Advanced Speech Signal Processing

Empirical Mode Decomposition for Advanced Speech Signal Processing

Non-Negative Matrix Factorization with Sparsity Learning for Single Channel Audio Source Separation

Speech Enhancement Using Hilbert Spectrum and Wavelet Packet Based Soft-Thresholding

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