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Music is a temporal organization of sounds, and we can therefore assume that any music representation has a structure that reflects some conceptual principles. This structure is hardly explicitly accessible in many encodings, such as, for instance, audio files. However, it appears much more clearly in the language of music notation.
We propose to u...
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... The human perception of sound is not linear, yet better approximated by logarithmic curves [99][100][101]. By converting the retrieved values to decibels (dB) the scale becomes logarithmic. The intensity of colors when this is plotted corresponds more closely to what a human hears than if one used a linear scale, which lets the network perceive the features as if a human is supervising. ...
Music genres can reveal our preferences and are one of the main tools for retailers, libraries, and people to organize music. In addition, the music industry uses genres as a key method to define and target different markets, and thus, being able to categorize genres is an asset for marketing and music production. Several pieces of research have been done to classify western music genres, yet nothing has been done to classify Persian music genres so far. In this research, a tailored deep neural network-based method, termed PMG-Net, is introduced to automatically classify Persian music genres. Also, to assess the PMG-Net, a dataset, named PMG-Data, consisting of 500 music from different genres of Pop, Rap, Traditional, Rock, and Monody are collected and labeled, which is made publicly available for researchers. The accuracy obtained by PMG-Net on the PMG-Data is 86%, indicating an acceptable performance of the method compared with the existing deep neural network-based approaches.
... Text-based operations raise the problem of independence with respect to the physical content encoding: it is a widely admitted principle, in the community that the result of the text of a query should not be tied to a specific representation, but rather defined with respect to a "logical" data model. We proposed such a model in [39][40][41], and the design presented in the present paper relies on such a high-level representation. ...
We address the problem of scalable content-based search in large collections of music documents. Music content is highly complex and versatile and presents multiple facets that can be considered independently or in combination. Moreover, music documents can be digitally encoded in many ways. We propose a general framework for building a scalable search engine, based on (i) a music description language that represents music content independently from a specific encoding, (ii) an extendible list of feature-extraction functions, and (iii) indexing, searching, and ranking procedures designed to be integrated into the standard architecture of a text-oriented search engine. As a proof of concept, we also detail an actual implementation of the framework for searching in large collections of XML-encoded music scores, based on the popular ElasticSearch system. It is released as open-source in GitHub, and available as a ready-to-use Docker image for communities that manage large collections of digitized music documents.
