This document presents the work done in automatic detection of music events present in audio signals. The signal corresponds to recordings of broadcast radio and TV programs. The aim of this work is the development of algorithms to discriminate musical segments from other sounds. The algorithms require the definition of models for audio classification, in our case Gaussian models. Two models were created, one for music and another for non-music (a background model representing speech in most of the times) and the classification is based on log likelihood ratios. In the first part of this work several hours of audio recordings have been manually annotated in order to define an audio database. The database includes two sets of audio f...
Signal processing methods for audio classification and music content analysis are developed in this ...
This thesis is about the automatic detection and classification of sound events (e.g. notes or percu...
This work was supported by the EPSRC Leadership Fellowship EP/G007144/1, by the EPSRC Research Grant...
This document presents the work done in automatic detection of music events present in audio signal...
The purpose it is to make the development of an algorithm that is able to extract the features of au...
En aquesta tesi presentem un mètode general per al reconeixement automàtic d’instruments musicals pa...
The purpose it is to make the development of an algorithm that is able to extract the features of au...
En aquesta tesi presentem un mètode general per al reconeixement automàtic d’instruments musicals pa...
For intelligent systems to make best use of the audio modality, it is important that they can recogn...
The goal of this project is to develop, implement and optimize an existing method called Continuous ...
The goal of this project is to develop, implement and optimize an existing method called Continuous ...
The present work describes the design, implementation and evaluation of a system for automatic audio...
Signal processing methods for audio classification and music content analysis are developed in this ...
In this paper we present a new methodology for detecting sound events in music signals using Gaussia...
Several factors affecting the automatic classification of musical audio signals are examined. Classi...
Signal processing methods for audio classification and music content analysis are developed in this ...
This thesis is about the automatic detection and classification of sound events (e.g. notes or percu...
This work was supported by the EPSRC Leadership Fellowship EP/G007144/1, by the EPSRC Research Grant...
This document presents the work done in automatic detection of music events present in audio signal...
The purpose it is to make the development of an algorithm that is able to extract the features of au...
En aquesta tesi presentem un mètode general per al reconeixement automàtic d’instruments musicals pa...
The purpose it is to make the development of an algorithm that is able to extract the features of au...
En aquesta tesi presentem un mètode general per al reconeixement automàtic d’instruments musicals pa...
For intelligent systems to make best use of the audio modality, it is important that they can recogn...
The goal of this project is to develop, implement and optimize an existing method called Continuous ...
The goal of this project is to develop, implement and optimize an existing method called Continuous ...
The present work describes the design, implementation and evaluation of a system for automatic audio...
Signal processing methods for audio classification and music content analysis are developed in this ...
In this paper we present a new methodology for detecting sound events in music signals using Gaussia...
Several factors affecting the automatic classification of musical audio signals are examined. Classi...
Signal processing methods for audio classification and music content analysis are developed in this ...
This thesis is about the automatic detection and classification of sound events (e.g. notes or percu...
This work was supported by the EPSRC Leadership Fellowship EP/G007144/1, by the EPSRC Research Grant...