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 Tiv.lib: An Open-Source Library for the Tonal Description of Musical Audio
In this paper, we present TIV.lib, an open-source library for the content-based tonal description of musical audio signals. Its main novelty relies on the perceptually-inspired Tonal Interval Vector space based on the Discrete Fourier transform, from which multiple instantaneous and global representations, descriptors and metrics are computed—e.g., harmonic change, dissonance, diatonicity, and musical key. The library is cross-platform, implemented in Python and the graphical programming language Pure Data, and can be used in both online and offline scenarios. Of note is its potential for enhanced Music Information Retrieval, where tonal descriptors sit at the core of numerous methods and applications.
Download A Study of Control Methods for Percussive Sound Synthesis Based on Gans
The process of creating drum sounds has seen significant evolution in the past decades. The development of analogue drum synthesizers, such as the TR-808, and modern sound design tools in Digital Audio Workstations led to a variety of drum timbres that defined entire musical genres. Recently, drum synthesis research has been revived with a new focus on training generative neural networks to create drum sounds. Different interfaces have previously been proposed to control the generative process, from low-level latent space navigation to high-level semantic feature parameterisation, but no comprehensive analysis has been presented to evaluate how each approach relates to the creative process. We aim to evaluate how different interfaces support creative control over drum generation by conducting a user study based on the Creative Support Index. We experiment with both a supervised method that decodes semantic latent space directions and an unsupervised Closed-Form Factorization approach from computer vision literature to parameterise the generation process and demonstrate that the latter is the preferred means to control a drum synthesizer based on the StyleGAN2 network architecture.
Download Improved Automatic Instrumentation Role Classification and Loop Activation Transcription
Many electronic music (EM) genres are composed through the activation of short audio recordings of instruments designed for seamless repetition—or loops. In this work, loops of key structural groups such as bass, percussive or melodic elements are labelled by the role they occupy in a piece of music through the task of automatic instrumentation role classification (AIRC). Such labels assist EM producers in the identification of compatible loops in large unstructured audio databases. While human annotation is often laborious, automatic classification allows for fast and scalable generation of these labels. We experiment with several deeplearning architectures and propose a data augmentation method for improving multi-label representation to balance classes within the Freesound Loop Dataset. To improve the classification accuracy of the architectures, we also evaluate different pooling operations. Results indicate that in combination with the data augmentation and pooling strategies, the proposed system achieves state-of-theart performance for AIRC. Additionally, we demonstrate how our proposed AIRC method is useful for analysing the structure of EM compositions through loop activation transcription.