MIRUM '12Pub Date : 2012-11-02DOI: 10.1145/2390848.2390854
Marius Kaminskas, Ignacio Fernández-Tobías, F. Ricci, Iván Cantador
{"title":"Knowledge-based music retrieval for places of interest","authors":"Marius Kaminskas, Ignacio Fernández-Tobías, F. Ricci, Iván Cantador","doi":"10.1145/2390848.2390854","DOIUrl":"https://doi.org/10.1145/2390848.2390854","url":null,"abstract":"In this paper we address a particular recommendation task: retrieving musicians suited for a place of interest (POI). We present a knowledge-based framework built upon the DBpedia ontology linking items from different domains. Graph-based algorithms are used for ranking and filtering items in a target domain (music) with respect to their relatedness to an input item in a source domain (POIs). By conducting user studies we found that users appreciate and judge more valuable the suggestions generated by the proposed approach when a novel weight spreading activation algorithm is used to compute the matching between musicians and POIs. Moreover, users perceive compositions of the suggested musicians as suited for the POIs.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126143185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MIRUM '12Pub Date : 2012-11-02DOI: 10.1145/2390848.2390855
Alfonso Pérez, M. Wanderley
{"title":"Learning and extraction of violin instrumental controls from audio signal","authors":"Alfonso Pérez, M. Wanderley","doi":"10.1145/2390848.2390855","DOIUrl":"https://doi.org/10.1145/2390848.2390855","url":null,"abstract":"Acquisition of instrumental gestures in musical performances is an important task used in different fields ranging from acoustics and sound synthesis to motor learning or electroacoustic performances. The most common approach for acquiring gestures is by means of a sensing system. The direct measurement involves the use of usually expensive sensors with some degree of intrusivity and generally entails complex setups. Indirect acquisition is based on the processing of the audio signal and it is usually informed on acoustical or physical properties of the sound or sound production mechanism. In this paper we present an indirect acquisition method of violin controls from an audio signal based on learning of empirical data that is previously collected with a highly accurate sensing system. The learning consists of training of statistical models with a database of multimodal data from violin performances. The database includes audio spectral features and instrumental controls (bow tilt, bow force, bow velocity, bowing distance to the bridge and played string) and is designed to sample most part of the violin performance control space. We expect that once the indirect acquisition system is trained, no sensors should be required, so the indirect acquisition becomes a low-cost and non-intrusive acquisition method.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131447045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}