Automatic cymbal classification

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia InformáticaMost of the research on automatic music transcription is focused on the transcription of pitched instruments, like the guitar and the piano. Little attention has been given to unpitched instruments, such as the drum kit, which is a collection of unpitched instruments. Yet, over the last few years this type of instrument started to garner more attention, perhaps due to increasing popularity of the drum kit in the western music. There has been work on automatic music transcription of the drum kit, especially the snare drum, bass drum, and hi-hat. Still, much work has to be done in order to achieve automatic music transcription of all unpitched instruments. An example of a type of unpitched instrument that has very particular acoustic characteristics and that has deserved almost no attention by the research community is the drum kit cymbals. A drum kit contains several cymbals and usually these are treated as a single instrument or are totally disregarded by automatic music classificators of unpitched instruments. We propose to fill this gap and as such, the goal of this dissertation is automatic music classification of drum kit cymbal events, and the identification of which class of cymbals they belong to. As stated, the majority of work developed on this area is mostly done with very different percussive instruments, like the snare drum, bass drum, and hi-hat. On the other hand, cymbals are very similar between them. Their geometry, type of alloys, spectral and sound traits shows us just that. Thus, the great achievement of this work is not only being able to correctly classify the different cymbals, but to be able to identify such similar instruments, which makes this task even harder

Timbral Learning for Musical Robots

abstract: The tradition of building musical robots and automata is thousands of years old. Despite this rich history, even today musical robots do not play with as much nuance and subtlety as human musicians. In particular, most instruments allow the player to manipulate timbre while playing; if a violinist is told to sustain an E, they will select which string to play it on, how much bow pressure and velocity to use, whether to use the entire bow or only the portion near the tip or the frog, how close to the bridge or fingerboard to contact the string, whether or not to use a mute, and so forth. Each one of these choices affects the resulting timbre, and navigating this timbre space is part of the art of playing the instrument. Nonetheless, this type of timbral nuance has been largely ignored in the design of musical robots. Therefore, this dissertation introduces a suite of techniques that deal with timbral nuance in musical robots. Chapter 1 provides the motivating ideas and introduces Kiki, a robot designed by the author to explore timbral nuance. Chapter 2 provides a long history of musical robots, establishing the under-researched nature of timbral nuance. Chapter 3 is a comprehensive treatment of dynamic timbre production in percussion robots and, using Kiki as a case-study, provides a variety of techniques for designing striking mechanisms that produce a range of timbres similar to those produced by human players. Chapter 4 introduces a machine-learning algorithm for recognizing timbres, so that a robot can transcribe timbres played by a human during live performance. Chapter 5 introduces a technique that allows a robot to learn how to produce isolated instances of particular timbres by listening to a human play an examples of those timbres. The 6th and final chapter introduces a method that allows a robot to learn the musical context of different timbres; this is done in realtime during interactive improvisation between a human and robot, wherein the robot builds a statistical model of which timbres the human plays in which contexts, and uses this to inform its own playing.Dissertation/ThesisDoctoral Dissertation Media Arts and Sciences 201

Timbral Analysis and Recording Parameter Transformations of Snare Drums

A snare drum is capable of producing a wide range of timbres influenced by playing technique, its physical construction, and the recording methods used. When a recording engineer configures drums and studio equipment, they adjust a plethora of real-world recording parameters to achieve the desired timbre. These recording parameters impart their own timbral properties by varying amounts, and in most cases the only way to modify these properties is to re-record the audio with changes applied to the real-world variables. This thesis examines methods for computational transformations of snare drum recordings to elicit perceptual changes that mimic modification of real-world recording variables. This is achieved through four main investigations, presented throughout this thesis, two which cover timbral analysis of snare drum recordings, and two which explore post-hoc recording parameter transformations. Strike velocity and microphone selection are factors known to affect snare drum timbre, the first study analyses timbral differences associated with snare drum strike velocity. Results show that listeners are able to distinguish between high and low velocity strikes using timbral cues alone, with microphone selection having no influence on this perceptual identification. Audio analysis reveals distinct temporal and spectral features, with higher velocity strikes producing greater energy in the lower mid-range and significantly longer decay times. The second study aims to demystify the subjective preference of different microphones for snare drum recording. For the majority of microphones, preference does not change between isolated strikes and those with the presence of bleed from the hi-hat and kick drum. On average, preference is higher for condenser microphones compared to dynamic. Additionally, spectral centroid and an objective measure of brightness positively correlate with subjective scores. The ability to perceptually modify drum recording parameters in a post-recording process would be of great benefit to engineers limited by time or equipment. The first post-hoc recording parameter transformation study focuses on microphone selection, mapping the spectral features from highly-preferred microphones onto a microphone with less favourable timbral characteristics. This investigation also details the development and evaluation of a robotic drum arm for consistent strike velocity. Subjective assessment reveals that participants show no preferences between recordings from highly-preferred microphones and those from a transformed least-preferred microphone. The last study employs a data-driven approach for post-recording modification of dampening and microphone position. The system consists of a autoencoder that analyses an audio input and predicts optimal parameters of one or more third-party audio effects, which process the audio to produce the desired transformations. Two novel audio effects are proposed and compared against existing audio plugins. Perceptual quality of transformations is assessed through a subjective listening test and an object evaluation is used to measure system performance, positive results demonstrate a capacity to emulate snare dampening

I.: Towards Timbre Recognition of Percussive Sounds

The development of computer algorithms for music instrument identification and parameter extraction in digital audio signals is an active research field. A musician can listen to music and instantly identify different instruments and the timbres produced by various playing techniques. Creating software to allow computers to do the same is much more challenging. This project will use digital signal processing and machine learning techniques to differentiate snare drum timbres produce