User Specific Adaptation in Automatic Transcription of Vocalised Percussion

TL;DR

This paper introduces LVT, a user-adaptive system for transcribing vocalised percussion sounds into drum patterns within DAWs, utilizing machine learning and feature selection for personalized accuracy.

Contribution

It presents a novel user-specific vocal percussion transcription system with feature selection, improving personalization in drum sound recognition.

Findings

Effective user-specific adaptation improves transcription accuracy.

The system successfully classifies vocalised drum sounds across different users.

Feature selection enhances the relevance of extracted audio features.

Abstract

The goal of this work is to develop an application that enables music producers to use their voice to create drum patterns when composing in Digital Audio Workstations (DAWs). An easy-to-use and user-oriented system capable of automatically transcribing vocalisations of percussion sounds, called LVT - Live Vocalised Transcription, is presented. LVT is developed as a Max for Live device which follows the `segment-and-classify' methodology for drum transcription, and includes three modules: i) an onset detector to segment events in time; ii) a module that extracts relevant features from the audio content; and iii) a machine-learning component that implements the k-Nearest Neighbours (kNN) algorithm for the classification of vocalised drum timbres. Due to the wide differences in vocalisations from distinct users for the same drum sound, a user-specific approach to vocalised transcription is proposed. In this perspective, a given end-user trains the algorithm with their own vocalisations for each drum sound before inputting their desired pattern into the DAW. The user adaption is achieved via a new Max external which implements Sequential Forward Selection (SFS) for choosing the most relevant features for a given set of input drum sounds.