Download Metrix: A Musical Data Definition Language and Data Structure for a Spectral Modeling Based Synthesizer
Since the MIDI 1.0 specification , well over 15 years ago, many have been the attempts to give a solution to all the limitations that soon became clear. None of these have had a happy ending, mainly due to commercial interests and as a result, when trying to find an appropriate synthesis control user interface, we had not many choices but the use of MIDI. That's the reason why the idea of defining a new user interface aroused. In this article, the main components of this interface will be discussed, paying special attention to the advantages and new features it reports to the enduser.
Download Sound Transformations Based on the SMS High Level Attributes
The basic Spectral Modeling Synthesis (SMS) technique models sounds as the sum of sinusoids plus a residual. Though this analysis/synthesis system has proved to be successful in transforming sounds, more powerful and intuitive musical transformations can be achieved by moving into the SMS high-level attribute plane. In this paper we describe how to extract high level sound attributes from the basic representation, modify them, and add them back before the synthesis stage. In this process new problems come up for which we propose some initial solutions.
Download The Origins of DAFx and its Future within the Sound and Music Computing Field
DAFX is an established conference that has become a reference gathering for the researchers working on audio signal processing. In this presentation I will go back ten years to the beginning of this conference and to the ideas that promoted it. Then I will jump to the present, to the current context of our research field, different from the one ten years ago, and I will make some personal reflections on the current situation and the challenges that we are encountering.
Download SMSPD, LIBSMS and a Real‐Time SMS Instrument
We present a real-time implementation of SMS synthesis in Pure Data. This instrument focuses on interaction with the ability to continuously synthesize any frame position within an SMS sound representation, in any order, thereby freeing time from other parameters such as frequency or spectral shape. The instrument can be controlled expressively with a Wacom Tablet that offers both coupled and absolute controls with good precision. A prototype graphical interface in python is presented that helps to interact with the SMS data through visualization. In this system, any sound sample with interesting spectral features turns into a playable instrument. The processing functionality originates in the SMS C code written almost 20 years ago, now re-factored into the open source library, libsms, also wrapped into a python module. A set of externals for Pure Data, called smspd, was made using this library to facilitate on-the-fly analysis, flexible modifications, and interactive synthesis. We discuss new transformations are introduced based on the possibilities of this system and ideas for higher-level, feature based transformations that benefit from the interactivity of this system.
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.