Speech separation using deep learning

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Speech signal is usually degraded by room reverberation and additive noises in real environments. This paper focuses on separating target speech signal in reverberant conditions from binaural inputs. Binaural separation is formulated as a supervised learning problem, and we employ deep learning to map from both spatial and spectral features to a training target. With binaural inputs, we first apply a fixed beamformer and then extract several spectral features.

A new spatial feature is proposed and extracted to complement the spectral features. The training target is the recently suggested ideal ratio mask. Systematic evaluations and comparisons show that the proposed system achieves very good separation performance and substantially outperforms related algorithms under challenging multi-source and reverberant environments. EVERYDAY listening scenarios are complex, with multiple concurrent sound sources and their reflections from the surfaces in physical space.

A solution to the cocktail party problem, also known as the speech separation problem, is important to many applications such as hearing aid design, robust automatic speech recognition ASR and mobile communication.

However, speech separation remains a technical challenge despite extensive research over decades. Since the target speech and background noise usually overlap in time and frequency, it is hard to remove the noise without speech distortion in monaural separation.

However, the speech and interfering sources are often located at different positions of the physical space, one can exploit the spatial information for speech separation by using two or more microphones.

Fixed and adaptive beamformers are common multi-microphone speech separation techniques [ 29 ]. The delay-and-sum beamformer is the simplest and most widely used fixed beamformer, which can be steered to a specified direction by adjusting phases for each microphone and adds the signals from different microphones. One limitation of a fixed beamformer is that it needs a large array to achieve high-fidelity separation.

Compared with fixed beamformers, adaptive beamformers provide better performance in certain conditions, like strong and relatively few interfering sources. The minimized variance distortionless response MVDR [ 10 ] beamformer is a representative adaptive beamformer, which minimizes the output energy while imposing linear constraints to maintain energies from the direction of the target speech. Adaptive beamforming can be converted into an unconstrained optimization problem by using a Generalized Sidelobe Canceller [ 12 ].

However, adaptive beamformers are more sensitive than fixed beamformers to microphone array errors such as sensor mismatch and mis-steering, and to correlated reflections arriving from outside the look direction [ 1 ]. The performance of both fixed and adaptive beamformers diminishes in the presence of room reverberation, particularly when target source is outside the critical distance at which direct-sound energy equals reverberation energy.

speech separation using deep learning

A different class of multi-microphone speech separation is based on Multichannel Wiener Filtering MWFwhich estimates the speech signal of the reference microphone in the minimum-mean-square-error sense by utilizing the correlation matrices of speech and noise. In contrast to beamforming, no assumption of target speech direction and microphone array structure needs to be made, while exhibiting a degree of robustness. The challenge for MWF is to estimate the correlation matrices of speech and noise, especially in non-stationary noise scenarios [ 26 ].

Another popular class of binaural separation methods is localization-based clustering [ 22 ] [ 38 ]. In general, two steps are taken.Documentation Help Center. This example shows how to isolate a speech signal using a deep learning network. The cocktail party effect refers to the ability of the brain to focus on a single speaker while filtering out other voices and background noise. Humans perform very well at the cocktail party problem. This example shows how to use a deep learning network to separate individual speakers from a speech mix where one male and one female are speaking simultaneously.

Load audio files containing male and female speech sampled at 4 kHz. Listen to the audio files individually for reference. Combine the two speech sources. Ensure the sources have equal power in the mix. Normalize the mix so that its max amplitude is one. Visualize the original and mix signals. Listen to the mixed speech signal. This example shows a source separation scheme that extracts the male and female sources from the speech mix. Use stft to visualize the time-frequency TF representation of the male, female, and mix speech signals.

Use a Hann window of lengthan FFT length ofand an overlap length of The application of a TF mask has been shown to be an effective method for separating desired audio signals from competing sounds. The mask is multiplied element-by-element with the underlying STFT to isolate the desired source. The TF mask can be binary or soft. In an ideal binary mask, the mask cell values are either 0 or 1.

Speech Separation Using Deep Learning

If the power of the desired source is greater than the combined power of other sources at a particular TF cell, then that cell is set to 1. Otherwise, the cell is set to 0. Visualize the estimated and original signals. Listen to the estimated male and female speech signals. In a soft mask, the TF mask cell value is equal to the ratio of the desired source power to the total mix power. TF cells have values in the range [0,1].

Compute the soft mask for the male speaker. Note that the results are very good because the mask is created with full knowledge of the separated male and female signals. The goal of the deep learning network in this example is to estimate the ideal soft mask described above.In general, humans communicate through speech.

Cocktail Party Source Separation Using Deep Learning Networks

The target speech which is known as the speech of interest is degraded by reverberation from surface reflections and extra noises from additional sound sources. Speech separation means separating the voices of various speakers or separating noises background interference from the original audio signal. Speech separation is helpful for a bountiful of applications. It is an extremely challenging task to build an automatic system for this purpose.

The information about the speaker or the source of the sound and the background noises are learned by training the machine with different data using supervised machine learning. The research work presented here is primarily partitioned into 3 parts i. Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide.

Speech Separation Using Deep Learning. Authors Authors and affiliations P. Conference paper First Online: 07 November This is a preview of subscription content, log in to check access.

Vinyals, O. Maas, A. Huang, P. Narayanan, A. Wang, Y. IEEE Trans. Audio Speech Lang. Cherry, E. Boll, S. Miller, G. Lyon, R. Wang, D. Trends Amplif. Hu, G. Neural Netw. Anzalone, M. Ear Hear. Brungart, D. Li, N. Accessed 21 April Sonnenburg, S. Delfarah, M. Cardoso, J. Nandal 1 Email author 1.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

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Deep Learning Based Binaural Speech Separation in Reverberant Environments

Email Address. Sign In. Deep Learning Based Speech Separation via NMF-Style Reconstructions Abstract: Deep learning based speech separation usually uses a supervised algorithm to learn a mapping function from noisy features to separation targets. These separation targets, either ideal masks or magnitude spectrograms, have prominent spectro-temporal structures. Nonnegative matrix factorization NMF is a well-known representation learning technique that is capable of capturing the basic spectral structures.

Therefore, the combination of deep learning and NMF as an organic whole is a smart strategy. In this paper, we propose a jointly combinatorial scheme to concentrate the strengths of both DNN and NMF for speech separation. NMF is used to learn the basis spectra that then are integrated into a DNN to directly reconstruct the magnitude spectrograms of speech and noise. Instead of predicting activation coefficients inferred by NMF, which is used as an intermediate target by the previous methods, DNN directly optimizes an actual separation objective in our system, so that the accumulated errors could be alleviated.

Moreover, we explore a discriminative training objective with sparsity constraints to suppress noise and preserve more speech components further. Systematic experiments show that the proposed models are competitive with the previous methods.

Article :. Date of Publication: 02 July DOI: Need Help?Deep clustering DC and utterance-level permutation invariant training uPIT have been demonstrated promising for speaker-independent speech separation. DC is usually formulated as two-step processes: embedding learning and embedding clustering, which results in complex separation pipelines and a huge obstacle in directly optimizing the actual separation objectives.

As for uPIT, it only minimizes the chosen permutation with the lowest mean square error, doesn't discriminate it with other permutations. In this paper, we propose a discriminative learning method for speaker-independent speech separation using deep embedding features. Firstly, a DC network is trained to extract deep embedding features, which contain each source's information and have an advantage in discriminating each target speakers.

Then these features are used as the input for uPIT to directly separate the different sources. Moreover, in order to maximize the distance of each permutation, the discriminative learning is applied to fine tuning the whole model. Our experiments are conducted on WSJmix dataset. Experimental results show that the proposed models achieve better performances than DC and uPIT for speaker-independent speech separation. Cunhang Fan. Bin Liu. Jianhua Tao. Jiangyan Yi. Zhengqi Wen. Monaural speech dereverberation is a very challenging task because no sp We propose a novel deep learning model, which supports permutation invar Deep neural networks DNNs have achieved substantial predictive perform We address talker-independent monaural speaker separation from the persp Multi-channel deep clustering MDC has acquired a good performance for Recently, deep clustering DPCL based speaker-independent speech separa Single-microphone, speaker-independent speech separation is normally per Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

Monaural speech separation aims to estimate target sources from mixed signals in a single-channel. It is a very challenging task, which is known as the cocktail party problem. In order to solve the cocktail party problem, many works have been done over the decades. However, these methods have led to very limited success in speaker-independent speech separation [ 5 ]. Recently, supervised methods using deep neural networks have significantly improved the performance of speech separation.

It trains a bidirectional long-short term memory BLSTM network to map the mixed spectrogram into an embedding space. At testing stage, the embedding vector of each time-frequency TF bin is clustered by K-means to obtain binary masks. To overcome this limitation, the deep attractor network DANet. The permutation invariant training PIT [ 14 ] and utterance-level PIT uPIT [ 15 ] are proposed to solve the label ambiguity or permutation problem of speech separation.

The PIT method solves this problem by minimizing the permutation with the lowest mean square error MSE at frame level. However, it does not solve the speaker tracing problem. To solve this problem, uPIT is proposed.We are forced to remove them due to our different mindset.

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speech separation using deep learning

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Demo and Architecture - Audio Source Separation p.1

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Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features

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