Download TorchFX: A Modern Approach to Audio DSP with PyTorch and GPU Acceleration The increasing complexity and real-time processing demands of
audio signals require optimized algorithms that utilize the computational power of Graphics Processing Units (GPUs).
Existing Digital Signal Processing (DSP) libraries often do not provide
the necessary efficiency and flexibility, particularly for integrating
with Artificial Intelligence (AI) models. In response, we introduce TorchFX: a GPU-accelerated Python library for DSP, engineered to facilitate sophisticated audio signal processing. Built on
the PyTorch framework, TorchFX offers an Object-Oriented interface similar to torchaudio but enhances functionality with a novel
pipe operator for intuitive filter chaining. The library provides a
comprehensive suite of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, with a focus on multichannel
audio, thereby facilitating the integration of DSP and AI-based
approaches.
Our benchmarking results demonstrate significant
efficiency gains over traditional libraries like SciPy, particularly
in multichannel contexts. While there are current limitations in
GPU compatibility, ongoing developments promise broader support and real-time processing capabilities. TorchFX aims to become a useful tool for the community, contributing to innovation
in GPU-accelerated DSP. TorchFX is publicly available on GitHub
at https://github.com/matteospanio/torchfx.
Download Information Retrieval of Marovany Zither Music Based on an Original Optical-Based System In this work, we introduced an original optical-based retrieval system dedicated to the music analysis of the marovany zither, a traditional instrument of Madagascar. From a humanistic perspective, our motivation for studying this particular instrument is its cultural importance due to its association with a possession ritual called tromba. The long-term goal of this work is to achieve a systematic classification of the marovany musical repertoire in this context of trance, and to classify the different recurrent musical patterns according to identifiable information. From an engineering perspective, we worked on the problem of competing signals in audio field recordings, e.g., from audience participation or percussion instruments. To overcome this problem, we recommended the use of a multichannel optical recording, putting forward technological qualities such as acquisition of independent signals corresponding to each string, high signal to noise ratio (high sensitivity to string displacement / low sensitivity to external sources), systematic inter-notes demarcation resulting from the finger-string contact. Optical signal characteristics greatly simplify the delicate task of automatic music transcription, especially when facing polyphonic music in noisy environment.
Download Hierarchical Organization and Visualization of Drum Sample Libraries Drum samples are an important ingredient for many styles of music. Large libraries of drum sounds are readily available. However, their value is limited by the ways in which users can explore them to retrieve sounds. Available organization schemes rely on cumbersome manual classification. In this paper, we present a new approach for automatically structuring and visualizing large sample libraries through audio signal analysis. In particular, we present a hierarchical user interface for efficient exploration and retrieval based on a computational model of similarity and self-organizing maps.
Download ALAMODE: Automated Learning of Acoustical Modal Parameters via Differential Evolution This paper is a technical report on the methodology submitted for Task A of the 1st DAFx Parameter Estimation Challenge. The goal of the challenge’s task is to invert the multi-dimensional physical and geometric parameters of a virtual plate reverberator given a target reference impulse response. To achieve this, we present a multi-stage gradient-free optimization framework. This three-stage optimization is computed using an efficient physics-based simulator, starting with an optimization of only mode frequency-determining physical parameters, followed by a 6-DoF parameter optimization with position-determining ones and a final phase for frequency- and position-independent mode amplitude estimation.
Download Unsupervised Taxonomy of Sound Effects Sound effect libraries are commonly used by sound designers in a range of industries. Taxonomies exist for the classification of sounds into groups based on subjective similarity, sound source or common environmental context. However, these taxonomies are not standardised, and no taxonomy based purely on the sonic properties of audio exists. We present a method using feature selection, unsupervised learning and hierarchical clustering to develop an unsupervised taxonomy of sound effects based entirely on the sonic properties of the audio within a sound effect library. The unsupervised taxonomy is then related back to the perceived meaning of the relevant audio features.
Download The Mix Evaluation Dataset Research on perception of music production practices is mainly concerned with the emulation of sound engineering tasks through lab-based experiments and custom software, sometimes with unskilled subjects. This can improve the level of control, but the validity, transferability, and relevance of the results may suffer from this artificial context. This paper presents a dataset consisting of mixes gathered in a real-life, ecologically valid setting, and perceptual evaluation thereof, which can be used to expand knowledge on the mixing process. With 180 mixes including parameter settings, close to 5000 preference ratings and free-form descriptions, and a diverse range of contributors from five different countries, the data offers many opportunities for music production analysis, some of which are explored here. In particular, more experienced subjects were found to be more negative and more specific in their assessments of mixes, and to increasingly agree with each other.
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 Analysis of Musical Dynamics in Vocal Performances Using Loudness Measures In addition to tone, pitch and rhythm, dynamics is one of the expressive dimensions of the performance of a music piece that has received limited attention. While the usage of dynamics may vary from artist to artist, and also from performance to performance, a systematic methodology to automatically identify the dynamics of a performance in terms of musically meaningful terms like forte, piano may offer valuable feedback in the context of music education and in particular in singing. To this end, we have manually annotated the dynamic markings of commercial recordings of popular rock and pop songs from the Smule Vocal Balanced (SVB) dataset which will be used as reference data. Then as a first step for our research goal, we propose a method to derive and compare singing voice loudness curves in polyphonic mixtures. Towards measuring the similarity and variation of dynamics, we compare the dynamics curves of the SVB renditions with the one derived from the original songs. We perform the same comparison using professionally produced renditions from a karaoke website. We relate high values of Spearman correlation coefficient found in some select student renditions and the professional renditions with accurate dynamics.
Download Power-Balanced Dynamic Modeling of Vactrols: Application to a VTL5C3/2 Vactrols, which consist of a photoresistor and a light-emitting element that are optically coupled, are key components in optical dynamic compressors. Indeed, the photoresistor’s program-dependent dynamic characteristics make it advantageous for automatic gain control in audio applications. Vactrols are becoming more and more difficult to find, while the interest for optical compression in the audio community does not diminish. They are thus good candidates for virtual analog modeling. In this paper, a model of vactrols that is entirely physical, passive, with a program-dependent dynamic behavior, is proposed. The model is based on first principles that govern semi-conductors, as well as the port-Hamiltonian systems formalism, which allows the modeling of nonlinear, multiphysical behaviors. The proposed model is identified with a real vactrol, then connected to other components in order to simulate a simple optical compressor.