The Importance of Cross Database Evaluation in Sound Classification

In numerous articles (Martin and Kim, 1998; Fraser and Fujinaga, 1999; and many others) sound classification algorithms are evaluated using "self classification" - the learning and test groups are randomly selected out of the same sound database. We will show that "self classification" is not necessarily a good statistic for the ability of a classification algorithm to learn, generalize or classify well. We introduce the alternative "Minus-1 DB" evaluation method and demonstrate that it does not have the shortcomings of "self classification"

Sistemas de classificação musical com redes neuronais

Como resultado da evolução e inovação tecnológicas, a indústria da distribuição electrónica de música tem tido um enorme crescimento. Desta forma, tarefas como a classificação automática de géneros musicais tornam-se um forte motivo para o incremento da investigação na área. O reconhecimento automático de géneros musicais envolve tarefas como a extracção de características das músicas e o desenvolvimento de classificadores que utilizem essas características. Neste estudo pretendeu-se, através de 3 problemas de classificação independentes, classificar peças de música clássica. Foi construído um protótipo para um sistema real de classificação, onde de um conjunto de músicas não catalogadas, foram automaticamente extraídos dez segmentos de seis segundos cada. Cada segmento musical foi classificado individualmente utilizando redes neuronais, tendo sido, para tal, extraídas 40 características por segmento. Cada música foi classificada no género mais representado pelos seus segmentos.As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. In this study we aim to classify classical music in subgenres, through three independent classification problems. Therefore, we extract 40 features for each one of the musical segments and we use neural nets as classifiers. Afterwards, due to the quality of the obtained results, a prototype system for automatic music classification of entire songs (not only segments) was built. We use 10 extracts for each song, uniformly distributed throughout the song. Each song is classified according to the most representative genre in all extracts

Gestural extraction from musical audio signals

Conventional exploration of gestures normally associated with musical instruments can be a costly and intrusive process. This thesis presents a novel approach to gestural extraction which overcomes these problems. The motivation behind this research is that the result of gestural input can be heard and therefore extracted from the acoustic signal produced by a musical instrument. Therefore, the guiding principles of this work are taken from the human auditory system. The concept of temporal grouping, and the fact that any sound which reaches the inner ear is conveyed to the brain, are two features of the auditory system that are mimicked by the presented system. Pertinent definitions are proposed for the sections of the note envelope and musical instrument gestures are classified according to those responsible for excitation or control. The extraction of gestural information is dependent upon successful identification of note events. A note tracking system is presented which exploits the structure of a note in order to perform preliminary note onset detection. A backtracking function is employed to regress through auditory data, providing a means of assigning individual start points to each note harmonic. The note tracking system also records the end point of each note harmonic. Note information is validated by a bespoke musical comparison system which provides a means of comparing and evaluating different note detection methods. Information provided by the note tracking system is used to extract gestural information regarding oboe key presses and excitation (articulation) methods of string instruments. System tests show that it is possible to correctly distinguish between bowed and plucked notes with an 89% success rate, using only three discriminators associated with the onset of a note. In this thesis the foundations of a multifacetted gestural extraction system are presented with useful potential for further development

Toward Real-Time Recognition of Acoustic Musical Instruments

Introduction In many musical situations, it is often useful to know not only the pitch of the sound but the instrument that is producing the sound. A real-time timbre recognition system based of Miller Puckette's fiddle program (Puckette et al. 1998) was tested using the attack portions of acoustic musical instruments (Puckette's accompanying program bonk is designed to recognizes timbre of percussion instruments). The dynamically changing spectra are quantified by the movment of centroid and other moments of the spectra. These and other features were stored in the database for an exemplar-based learning system, which is based on a k-nearest neighbor classifier. The system is enhanced by a genetic algorithm, which finds the optimal set of feature weights to improve the recognition rate. 2. Features involving moments The method of moments is a versatile tool for decomposing arbitrary shape into a finite set of character features. In general, moments describe