Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/6124
Título: Automatic musical instrument recognition for multimedia indexing
Autor: Malheiro, Frederico Alberto Santos de Carteado
Orientador: Cavaco, Sofia
Palavras-chave: Audio indexing
Non-negative matrix factorization
Instrument recognition
Ambient sound recognition
Machine learning
Data de Defesa: 2011
Editora: Faculdade de Ciências e Tecnologia
Resumo: The subject of automatic indexing of multimedia has been a target of numerous discussion and study. This interest is due to the exponential growth of multimedia content and the subsequent need to create methods that automatically catalogue this data. To fulfil this idea, several projects and areas of study have emerged. The most relevant of these are the MPEG-7 standard, which defines a standardized system for the representation and automatic extraction of information present in the content, and Music Information Retrieval (MIR), which gathers several paradigms and areas of study relating to music. The main approach to this indexing problem relies on analysing data to obtain and identify descriptors that can help define what we intend to recognize (as, for instance,musical instruments, voice, facial expressions, and so on), this then provides us with information we can use to index the data. This dissertation will focus on audio indexing in music, specifically regarding the recognition of musical instruments from recorded musical notes. Moreover, the developed system and techniques will also be tested for the recognition of ambient sounds (such as the sound of running water, cars driving by, and so on). Our approach will use non-negative matrix factorization to extract features from various types of sounds, these will then be used to train a classification algorithm that will be then capable of identifying new sounds.
Descrição: Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática
URI: http://hdl.handle.net/10362/6124
Aparece nas colecções:FCT: DI - Dissertações de Mestrado

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