Musical onset detection is a key component in any beat tracking system. Existing algorithms for onset detection usually rely on short time Fourier transform (STFT). Of numerous existing methods to detect musical onsets, a common limitation is the genre-specific performance. For example, accurate onset locations are not usually identified for genres such as classical music and opera music. In th...

The study of musical expressivity is an active field in sound and music computing. The research interest comes from different motivations: to understand or model music expressivity; to identify the expressive resources that characterize an instrument, musical genre, or performer; or to build synthesis systems able to play expressively. In this paper, we present a system that focuses on the stud...

This paper discusses an approach for the feature extraction for audio genre classification and many other tasks of music information retrieval (MIR). Many musical genres are characterized not only by timbral information but also by temporal features such as rhythm patterns and bass-line patterns. In particular, modern music pieces mostly have certain fixed rhythm and bass-line patterns per genr...

In this paper we present the new genre of interactive operas implemented on personal computers. They differ from traditional ones not only because they are virtual, but mainly because they offer to composers and listeners new perspectives of combinations and interactions between music, text and visual aspects. 1 What Are Virtual Operas ? We define as a virtual interactive opera any opera implem...

This paper discusses an approach for the feature extraction for audio genre classification and many other tasks of music information retrieval (MIR). Many musical genres are characterized not only by timbral information but also by temporal features such as rhythm patterns and bass-line patterns. In particular, modern music pieces mostly have certain fixed rhythm and bass-line patterns per genr...

This thesis details theoretical and empirical work that draws from two main subject areas: Machine Learning (ML) and Digital Signal Processing (DSP). A unified general framework is given for the application of sparse machine learning methods to multivariate signal processing. In particular, methods that enforce sparsity will be employed for reasons of computational efficiency, regularisation, a...

Several works proposed for the automatic genre musical classification are based on various combinations of parameters, exploiting different models. However, the comparison of all previous works remain impossible since they used different target taxonomies, genre definitions and databases. In this paper, the world largest music database (Real World Computing) is used. Also, different measures re...

To help maximise the usefulness of MIR technologies in the wider community, we conducted an ethnographic study of music lessons in secondary schools in London, UK. The purpose is to understand better how musical concepts are negotiated with and without technology, so we can understand when and how MIR tools might be useful. We report on some of the themes uncovered, both about the range of tech...