A set of features is evaluated for recognition of musical instruments out of monophonic musical signals. Aiming to achieve a compact representation, the adopted features regard only spectral characteristics of sound and are limited in number. On top of these descriptors, various classification methods are implemented and tested. Over a dataset of 1007 tones from 27 musical instruments, support vector machines and quadratic discriminant analysis show comparable results with success rates close to 70% of successful classifications. Canonical discriminant analysis never had momentous results, while nearest neighbours performed on average among the employed classifiers. Strings have been the most misclassified instrument family, while very satisfactory results have been obtained with brass and woodwinds. The most relevant features are demonstrated to be the inharmonicity, the spectral centroid, and the energy contained in the first partial
A set of features is evaluated for recognition of musical instruments out of monophonic musical signals. Aiming to achieve a compact representation, the adopted features regard only spectral characteristics of sound and are limited in number. On top of these descriptors, various classification methods are implemented and tested. Over a dataset of 1007 tones from 27 musical instruments, support vector machines and quadratic discriminant analysis show comparable results with success rates close to 70% of successful classifications. Canonical discriminant analysis never had momentous results, while nearest neighbours performed on average among the employed classifiers. Strings have been the most misclassified instrument family, while very satisfactory results have been obtained with brass and woodwinds. The most relevant features are demonstrated to be the inharmonicity, the spectral centroid, and the energy contained in the first partial.
As the dimension and number of digital music archives grow, the problem of storing and accessing multimedia data is no longer confined to the database area. Specific approaches for music information retrieval are necessary to establish a connection between textual and content-based metadata. This article addresses such issues with the intent of surveying our perspective on music information retrieval. In particular, we stress the use of symbolic information as a central element in a complex musical environment. Musical themes, harmonies, and styles are automatically extracted from electronic music scores and employed as access keys to data. The database schema is extended to handle audio recordings. A score/audio matching module provides a temporal relationship between a music performance and the score played. Besides standard free-text search capabilities, three levels of retrieval strategies are employed. Moreover, the introduction of a hierarchy of input modalities assures meeting the needs and matching the expertise of a wide group of users. Singing, playing, and notating melodic excerpts is combined with more advanced musicological queries, such as querying by a sequence of chords. Finally, we present some experimental results and our future research directions.
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