Echo state networks (ESNs) are a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It has been demonstrated that ESNs outperform other methods on a number of benchmark tasks. Although the approach is appealing, there are still some inherent limitations in the original formulation.

Here we suggest two enhancements of this network model.
First, the previously proposed idea of filters in neurons is extended to arbitrary infinite impulse response (IIR) filter neurons. This enables such networks to learn multiple attractors and signals at different timescales, which is especially important for modeling real-world time series.
Second, a delay&sum readout is introduced, which adds trainable delays in the synaptic connections of output neurons and therefore vastly improves the memory capacity of echo state networks.

It is shown in commonly used benchmark tasks and real-world examples, that this new structure is able to significantly outperform standard ESNs and other state-of-the-art models for nonlinear dynamical system modeling.

This paper proposes reservoir computing as a general framework for nonlinear audio processing.
Reservoir computing is a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It can in theory approximate arbitrary nonlinear dynamical systems with arbitrary precision, has an inherent temporal processing capability and is therefore well suited for many nonlinear audio processing problems. Always when nonlinear relationships are present in the data and time information is crucial, reservoir computing can be applied.

Examples from three application areas are presented: nonlinear system identification of a tube amplifier emulator algorithm, nonlinear audio prediction, as necessary in a wireless transmission of audio where dropouts may occur, and automatic melody transcription out of a polyphonic audio stream, as one example from the big field of music information retrieval.
Reservoir computing was able to outperform state-of-the-art alternative models in all studied tasks.

Echo State Networks with Filter Neurons and a Delay&Sum Readout with Applications in Audio Signal Processing

Echo state networks (ESNs) are a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. They can in theory approximate arbitrary nonlinear dynamical system with arbitrary precision (universal approximation property), have an inherent temporal processing capability, and are therefore a very powerful enhancement of linear blackbox modeling techniques in nonlinear domain. It was demonstrated on a number of benchmark tasks, that echo state networks outperform other methods for nonlinear dynamical modeling.

This thesis suggests two enhancements of the original network model. First, the previously proposed idea of filters in neurons is extended to arbitrary infinite impulse response (IIR) filter neurons and the ability of such networks to learn multiple attractors is demonstrated. Second, a delay&sum readout is introduced, which adds trainable delays in the synaptic connections of output neurons and therefore vastly improves the memory capacity of echo state networks. It is shown in benchmark tasks that this new structure is able to outperform standard ESNs and other models, moreover no other comparable method for sparse nonlinear system identification with long-term dependencies could be found in literature.

Finally real-world applications in the context of audio signal processing are presented and compared to state-of-the-art alternative methods. The first example is a nonlinear system identification task of a tube amplifier and afterwards ESNs are trained for nonlinear audio prediction, as necessary in audio restoration or in the wireless transmission of audio where dropouts may occur. Furthermore an efficient and open source C++ library for echo state networks was developed and is briefly presented.

The audio examples can be downloaded below.

In this article echo state networks, a special form of recurrent neural networks, are discussed in the area of nonlinear audio signal processing. Echo state networks are a novel approach in recurrent neural networks with a very easy (linear) training algorithm.
Signal processing examples in nonlinear system identification (valve distortion, clipping), inverse modeling (quality enhancement) and audio prediction are briefly presented and discussed.

The goal of this workshop is to show how to position sound in space (stereo, multichannel and binaural). This should be done from a user point of view, without explaining the detailed mathematics behind the algorithms. Therefore existing and open-source implementations in Pure Data will be used and explained.

Topics:

• stereo-panning methods
• vector based amplitude panning (VBAP)
• ambisonic
• binaural ambisonic and 3D room simulation

To all topics I will explain the handling of the Pd implementations and the advantages/disadvantages of the specific methods, demonstrated on examples.

All workshop materials can be downloaded below.

Audio Texture ist eine Methode, aus einem gegebenen kurzen Audiobeispiel einen beliebig langen Audiostream zu generieren.
Das Audiobeispiel wird dabei zuerst analysiert und anschließend anhand von spektralen Eigenschaften (MFCC) in kleinere Teile unterteilt. Letzlich wird nun aus diesen ein beliebig langer Audiostream generiert.
Diese Methode kann ebenfalls verwendet werden, um längere Drop-Outs in einem Audiostream auszubessern bzw. aufzufüllen (Audio Texture Restauration).