Download Speech Dereverberation Using Recurrent Neural Networks Advances in deep learning have led to novel, state-of-the-art techniques for blind source separation, particularly for the application of non-stationary noise removal from speech. In this paper, we show how a simple reformulation allows us to adapt blind source separation techniques to the problem of speech dereverberation and, accordingly, train a bidirectional recurrent neural network (BRNN) for this task. We compare the performance of the proposed neural network approach with that of a baseline dereverberation algorithm based on spectral subtraction. We find that our trained neural network quantitatively and qualitatively outperforms the baseline approach.
Download Notes on the use of Variational Autoencoders for Speech and Audio Spectrogram Modeling Variational autoencoders (VAEs) are powerful (deep) generative artificial neural networks. They have been recently used in several papers for speech and audio processing, in particular for the modeling of speech/audio spectrograms. In these papers, very poor theoretical support is given to justify the chosen data representation and decoder likelihood function or the corresponding cost function used for training the VAE. Yet, a nice theoretical statistical framework exists and has been extensively presented and discussed in papers dealing with nonnegative matrix factorization (NMF) of audio spectrograms and its application to audio source separation. In the present paper, we show how this statistical framework applies to VAE-based speech/audio spectrogram modeling. This provides the latter insights on the choice and interpretability of data representation and model parameterization.
Download Modelling of nonlinear state-space systems using a deep neural network In this paper we present a new method for the pseudo black-box modelling of general continuous-time state-space systems using a discrete-time state-space system with an embedded deep neural network. Examples are given of how this method can be applied to a number of common nonlinear electronic circuits used in music technology, namely two kinds of diode-based guitar distortion circuits and the lowpass filter of the Korg MS-20 synthesizer.
Download Real-Time Black-Box Modelling With Recurrent Neural Networks This paper proposes to use a recurrent neural network for black-box modelling of nonlinear audio systems, such as tube amplifiers and distortion pedals. As a recurrent unit structure, we test both Long Short-Term Memory and a Gated Recurrent Unit. We compare the proposed neural network with a WaveNet-style deep neural network, which has been suggested previously for tube amplifier modelling. The neural networks are trained with several minutes of guitar and bass recordings, which have been passed through the devices to be modelled. A real-time audio plugin implementing the proposed networks has been developed in the JUCE framework. It is shown that the recurrent neural networks achieve similar accuracy to the WaveNet model, while requiring significantly less processing power to run. The Long Short-Term Memory recurrent unit is also found to outperform the Gated Recurrent Unit overall. The proposed neural network is an important step forward in computationally efficient yet accurate emulation of tube amplifiers and distortion pedals.
Download NMF Toolbox: Music Processing Applications of Nonnegative Matrix Factorization Nonnegative matrix factorization (NMF) is a family of methods widely used for information retrieval across domains including text, images, and audio. Within music processing, NMF has been used for tasks such as transcription, source separation, and structure analysis. Prior work has shown that initialization and constrained update rules can drastically improve the chances of NMF converging to a musically meaningful solution. Along these lines we present the NMF toolbox, containing MATLAB and Python implementations of conceptually distinct NMF variants—in particular, this paper gives an overview for two algorithms. The first variant, called nonnegative matrix factor deconvolution (NMFD), extends the original NMF algorithm to the convolutive case, enforcing the temporal order of spectral templates. The second variant, called diagonal NMF, supports the development of sparse diagonal structures in the activation matrix. Our toolbox contains several demo applications and code examples to illustrate its potential and functionality. By providing MATLAB and Python code on a documentation website under a GNU-GPL license, as well as including illustrative examples, our aim is to foster research and education in the field of music processing.
Download An FPGA-Based Accelerator for Sound Field Rendering Finite difference time domain (FDTD) schemes are widely applied to analyse sound propagation, but are computation-intensive and memory-intensive. Current sound field rendering systems with FDTD schemes are mainly based on software simulations on personal computers (PCs) or general-purpose graphic processing units (GPGPUs). In this research, an accelerator is designed and implemented using the field programmable gate array (FPGA) for sound field rendering. Unlike software simulations on PCs and GPGPUs, the FPGA-based sound field rendering system directly implements wave equations by reconfigurable hardware. Furthermore, a sliding window-based data buffering system is adopted to alleviate external memory bandwidth bottlenecks. Compared to the software simulation carried out on a PC with 128 GB DDR4 RAMs and an Intel i7-7820X processor running at 3.6 GHz, the proposed FPGA-based accelerator takes half of the rendering time and doubles the computation throughput even if the clock frequency of the FPGA system is about 267 MHz.
Download Synthetic Transaural Audio Rendering (STAR): a Perceptive Approach for Sound Spatialization The principles of Synthetic Transaural Audio Rendering (STAR) were first introduced at DAFx-06. This is a perceptive approach for sound spatialization, whereas state-of-the-art methods are rather physical. With our STAR method, we focus neither on the wave field (such as HOA) nor on the sound wave (such as VBAP), but rather on the acoustic paths traveled by the sound to the listener ears. The STAR method consists in canceling the cross-talk signals between two loudspeakers and the ears of the listener (in a transaural way), with acoustic paths not measured but computed by some model (thus synthetic). Our model is based on perceptive cues, used by the human auditory system for sound localization. The aim is to give the listener the sensation of the position of each source, and not to reconstruct the corresponding acoustic wave or field. This should work with various loudspeaker configurations, with a large sweet spot, since the model is neither specialized for a specific configuration nor individualized for a specific listener. Experimental tests have been conducted in 2015 and 2019 with different rooms and audiences, for still, moving, and polyphonic musical sounds. It turns out that the proposed method is competitive with the state-of-the-art ones. However, this is a work in progress and further work is needed to improve the quality.
Download Data Augmentation for Instrument Classification Robust to Audio Effects Reusing recorded sounds (sampling) is a key component in Electronic Music Production (EMP), which has been present since its early days and is at the core of genres like hip-hop or jungle. Commercial and non-commercial services allow users to obtain collections of sounds (sample packs) to reuse in their compositions. Automatic classification of one-shot instrumental sounds allows automatically categorising the sounds contained in these collections, allowing easier navigation and better characterisation. Automatic instrument classification has mostly targeted the classification of unprocessed isolated instrumental sounds or detecting predominant instruments in mixed music tracks. For this classification to be useful in audio databases for EMP, it has to be robust to the audio effects applied to unprocessed sounds. In this paper we evaluate how a state of the art model trained with a large dataset of one-shot instrumental sounds performs when classifying instruments processed with audio effects. In order to evaluate the robustness of the model, we use data augmentation with audio effects and evaluate how each effect influences the classification accuracy.
Download Time Scale Modification of Audio Using Non-Negative Matrix Factorization This paper introduces an algorithm for time-scale modification of audio signals based on using non-negative matrix factorization. The activation signals attributed to the detected components are used for identifying sound events. The segmentation of these events is used for detecting and preserving transients. In addition, the algorithm introduces the possibility of preserving the envelopes of overlapping sound events while globally modifying the duration of an audio clip.
Download Visualaudio-Design – Towards a Graphical Sounddesign VisualAudio-Design (VAD) is a spectral-node based approach to visually design audio collages and sounds. The spectrogram as a visualization of the frequency-domain can be intuitively manipulated with tools known from image processing. Thereby, a more comprehensible sound design is described to address common abstract interfaces for DSP algorithms that still use direct value inputs, sliders, or knobs. In addition to interaction in the timedomain of audio and conventional analysis and restoration tasks, there are many new possibilities for spectral manipulation of audio material. Here, affine transformations and two-dimensional convolution filters are proposed.