In recent years, wsj0-2mix has become the reference dataset for single-channel speech separation. Most deep learning-based speech separation models today are benchmarked on it. However, recent studies have shown important performance drops when models trained on wsj0-2mix are evaluated on other, similar datasets. To address this generalization issue, we created LibriMix, an open-source alternative to wsj0-2mix, and to its noisy extension, WHAM!. Based on LibriSpeech, LibriMix consists of two- or three-speaker mixtures combined with ambient noise samples from WHAM!. Using Conv-TasNet, we achieve competitive performance on all LibriMix versions. In order to fairly evaluate across datasets, we introduce a third test set based on VCTK for speech and WHAM! for noise. Our experiments show that the generalization error is smaller for models trained with LibriMix than with WHAM!, in both clean and noisy conditions. Aiming towards evaluation in more realistic, conversation-like scenarios, we also release a sparsely overlapping version of LibriMix's test set.

LibriMix: An Open-Source Dataset for Generalizable Speech Separation

We provide a speech coding scheme employing a generative model based on SampleRNN that, while operating at significantly lower bitrates, matches or surpasses the perceptual quality of state-of-the-art classic wide-band codecs. Moreover, it is demonstrated that the proposed scheme can provide a meaningful rate-distortion trade-off without retraining. We evaluate the proposed scheme in a series of listening tests and discuss limitations of the approach.

High-quality Speech Coding with Sample RNN

Nowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregation of those entries ultimately defines an organization's financial statement. To detect potential misstatements and fraud, international audit standards demand auditors to directly assess journal entries using 'Computer Assisted AuditTechniques' (CAATs). At the same time, discoveries in deep learning research revealed that machine learning models are vulnerable to 'adversarial attacks'. It also became evident that such attack techniques can be misused to generate 'Deepfakes' designed to directly attack the perception of humans by creating convincingly altered media content. The research of such developments and their potential impact on the finance and accounting domain is still in its early stage. We believe that it is of vital relevance to investigate how such techniques could be maliciously misused in this sphere. In this work, we show an adversarial attack against CAATs using deep neural networks. We first introduce a real-world 'thread model' designed to camouflage accounting anomalies such as fraudulent journal entries. Second, we show that adversarial autoencoder neural networks are capable of learning a human interpretable model of journal entries that disentangles the entries latent generative factors. Finally, we demonstrate how such a model can be maliciously misused by a perpetrator to generate robust 'adversarial' journal entries that mislead CAATs.

Adversarial Learning of Deepfakes in Accounting

A deep learning based time domain single-channel speech enhancement technique using multilayer encoder-decoder and a temporal convolutional network is proposed for use in applications such as smart speakers and voice assistants. The technique uses encoder-decoder with convolutional layers for obtaining representation suitable for speech enhancement and a temporal convolutional network (TCN) based separator between the encoder and decoder to learn long-range dependencies. The technique derives inspiration from speech separation techniques that use TCN based separator between a single layer encoder-decoder. We propose to use a multilayer encoder-decoder to obtain a noise-independent representation useful for separating clean speech and noise. We present t-SNE –based analysis of the representation learned using different architectures for selecting the optimal number of encoderdecoder layers. We evaluate the proposed architectures using an objective measure of speech quality, scale-invariant source-tonoise ratio, and by obtaining word error rate on a speech recognition platform. The proposed two-layer encoder-decoder architecture resulted in 48% improvement in WER over unprocessed noisy data and 33% and 44% improvement in WER over two baselines.

Improved Speech Enhancement Using TCN with Multiple Encoder-Decoder Layers.

We investigate which loss functions provide better separations via benchmarking an extensive set of those for music source separation. To that end, we first survey the most representative audio source separation losses we identified, to later consistently benchmark them in a controlled experimental setup. We also explore using such losses as evaluation metrics, via cross-correlating them with the results of a subjective test. Based on the observation that the standard signal-to-distortion ratio metric can be misleading in some scenarios, we study alternative evaluation metrics based on the considered losses.

/pdf/on-loss-functions-and-evaluation-metrics-for-music-source-1nc7nywm.pdf

On Loss Functions and Evaluation Metrics for Music Source Separation

Conv-TasNet is a recently proposed waveform-based deep neural network that achieves state-of-the-art performance in speech source separation. Its architecture consists of a learnable encoder/decoder and a separator that operates on top of this learned space. Various improvements have been proposed to Conv-TasNet. However, they mostly focus on the separator, leaving its encoder/decoder as a (shallow) linear operator. In this paper, we conduct an empirical study of Conv-TasNet and propose an enhancement to the encoder/decoder that is based on a (deep) non-linear variant of it. In addition, we experiment with the larger and more diverse LibriTTS dataset and investigate the generalization capabilities of the studied models when trained on a much larger dataset. We propose cross-dataset evaluation that includes assessing separations from the WSJ0-2mix, LibriTTS and VCTK databases. Our results show that enhancements to the encoder/decoder can improve average SI-SNR performance by more than 1 dB. Furthermore, we offer insights into the generalization capabilities of Conv-TasNet and the potential value of improvements to the encoder/decoder.

An Empirical Study of Conv-Tasnet

Here we present a novel approach to conditioning the SampleRNN generative model for voice conversion (VC). Conventional methods for VC modify the perceived speaker identity by converting between source and target acoustic features. Our approach focuses on preserving voice content and depends on the generative network to learn voice style. We first train a multi-speaker SampleRNN model conditioned on linguistic features, pitch contour, and speaker identity using a multi-speaker speech corpus. Voice-converted speech is generated using linguistic features and pitch contour extracted from the source speaker, and the target speaker identity. We demonstrate that our system is capable of many-to-many voice conversion without requiring parallel data, enabling broad applications. Subjective evaluation demonstrates that our approach outperforms conventional VC methods.

Voice Conversion with Conditional SampleRNN.

pdf/an-empirical-study-of-conv-tasnet-oclnrfevxw.pdf

An empirical study of Conv-TasNet

Virtual Reality (VR) audio scenes may be composed of a very large number of audio elements, including dynamic audio objects, fixed audio channels and scene-based audio elements such as Higher Order Ambisonics (HOA). Potentially, the subjective listening experience may be replicated using a compact spatial format with a set number of dynamic objects and scene-based elements, retaining only the perceptual essence of the audio scene. The compact format would further enable a reduction in the complexity of subsequent compression and rendering. This paper investigates these hypotheses by exploring the use of a compact format that consists of up to four dynamic objects and nine HOA channels, with the Enhanced Voice Services (EVS) codec being applied to a 4-channel down-mix of the compact format.

Immersive Audio Coding for Virtual Reality Using a Metadata-assisted Extension of the 3GPP EVS Codec

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An Empirical Study of Conv-Tasnet

Voice Conversion with Conditional SampleRNN.

An empirical study of Conv-TasNet

Immersive Audio Coding for Virtual Reality Using a Metadata-assisted Extension of the 3GPP EVS Codec