A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders

Recent studies have explored the use of deep generative models of speech spectra based of variational autoencoders (VAEs), combined with unsupervised noise models, to perform speech enhancement. These studies developed iterative algorithms involving either Gibbs sampling or gradient descent at each step, making them computationally expensive. This paper proposes a variational inference method to iteratively estimate the power spectrogram of the clean speech. Our main contribution is the analytical derivation of the variational steps in which the en-coder of the pre-learned VAE can be used to estimate the varia-tional approximation of the true posterior distribution, using the very same assumption made to train VAEs. Experiments show that the proposed method produces results on par with the afore-mentioned iterative methods using sampling, while decreasing the computational cost by a factor 36 to reach a given performance .Comment: Submitted to INTERSPEECH 201

TasNet: time-domain audio separation network for real-time, single-channel speech separation

Robust speech processing in multi-talker environments requires effective speech separation. Recent deep learning systems have made significant progress toward solving this problem, yet it remains challenging particularly in real-time, short latency applications. Most methods attempt to construct a mask for each source in time-frequency representation of the mixture signal which is not necessarily an optimal representation for speech separation. In addition, time-frequency decomposition results in inherent problems such as phase/magnitude decoupling and long time window which is required to achieve sufficient frequency resolution. We propose Time-domain Audio Separation Network (TasNet) to overcome these limitations. We directly model the signal in the time-domain using an encoder-decoder framework and perform the source separation on nonnegative encoder outputs. This method removes the frequency decomposition step and reduces the separation problem to estimation of source masks on encoder outputs which is then synthesized by the decoder. Our system outperforms the current state-of-the-art causal and noncausal speech separation algorithms, reduces the computational cost of speech separation, and significantly reduces the minimum required latency of the output. This makes TasNet suitable for applications where low-power, real-time implementation is desirable such as in hearable and telecommunication devices.Comment: Camera ready version for ICASSP 2018, Calgary, Canad

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

End-to-end non-negative auto-encoders: a deep neural alternative to non-negative audio modeling

Over the last decade, non-negative matrix factorization (NMF) has emerged as one of the most popular approaches to modeling audio signals. NMF allows us to factorize the magnitude spectrogram to learn representative spectral bases that can be used for a wide range of applications. With the recent advances in deep learning, neural networks (NNs) have surpassed NMF in terms of performance. However, these NNs are trained discriminatively and lack several key characteristics like re-usability and robustness, compared to NMF. In this dissertation, we develop and investigate the idea of end-to-end non-negative autoencoders (NAEs) as an updated deep learning based alternative framework to non-negative audio modeling. We show that end-to-end NAEs combine the modeling advantages of non-negative matrix factorization and the generalizability of neural networks while delivering significant improvements in performance. To this end, we first interpret NMF as a NAE and show that the two approaches are equivalent semantically and in terms of source separation performance. We exploit the availability of sophisticated neural network architectures to propose several extensions to NAEs. We also demonstrate that these modeling improvements significantly boost the performance of NAEs. In audio processing applications, the short-time fourier transform~(STFT) is used as a universal first step and we design algorithms and neural networks to operate on the magnitude spectrograms. We interpret the sequence of steps involved in computing the STFT as additional neural network layers. This enables us to propose end-to-end processing pipelines that operate directly on the raw waveforms. In the context of source separation, we show that end-to-end processing gives a significant improvement in performance compared to existing spectrogram based methods. Furthermore, to train these end-to-end models, we investigate the use of cost functions that are derived from objective evaluation metrics as measured on waveforms. We present subjective listening test results that reveal insights into the performance of these cost functions for end-to-end source separation. Combining the adaptive front-end layers with NAEs, we propose end-to-end NAEs and show how they can be used for end-to-end generative source separation. Our experiments indicate that these models deliver separation performance comparable to that of discriminative NNs, while retaining the modularity of NMF and the modeling flexibility of neural networks. Finally, we present an approach to train these end-to-end NAEs using mixtures only, without access to clean training examples