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Multiple fundamental frequency estimation using Gaussian smoothness
The goal of a polyphonic music transcription system is to extract a score from an audio signal. A multiple fundamental frequency estimator is the main piece of these systems, whereas tempo detection and key estimation complement them to correctly extract the score. In this work, in order to detect t...
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| creator | Pertusa, A. Inesta, J.M. |
| description | The goal of a polyphonic music transcription system is to extract a score from an audio signal. A multiple fundamental frequency estimator is the main piece of these systems, whereas tempo detection and key estimation complement them to correctly extract the score. In this work, in order to detect the fundamental frequencies that are present in a signal, a set of candidates are selected from the spectrum, and all their possible combinations are generated. The best combination is chosen in a frame by frame analysis by applying a set of rules, taking into account the harmonic amplitudes and the spectral smoothness measure described in this work. The system was evaluated and compared to other works, yielding competitive results and performance. |
| doi_str_mv | 10.1109/ICASSP.2008.4517557 |
| format | conference_proceeding |
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A multiple fundamental frequency estimator is the main piece of these systems, whereas tempo detection and key estimation complement them to correctly extract the score. In this work, in order to detect the fundamental frequencies that are present in a signal, a set of candidates are selected from the spectrum, and all their possible combinations are generated. The best combination is chosen in a frame by frame analysis by applying a set of rules, taking into account the harmonic amplitudes and the spectral smoothness measure described in this work. 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The system was evaluated and compared to other works, yielding competitive results and performance.</description><subject>Acoustic applications</subject><subject>Acoustic measurements</subject><subject>acoustic signal analysis</subject><subject>Acoustic signal processing</subject><subject>Frequency estimation</subject><subject>Gaussian distributions</subject><subject>Harmonic analysis</subject><subject>Multiple signal classification</subject><subject>Music</subject><subject>Signal analysis</subject><subject>Signal generators</subject><subject>Spectral analysis</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424414833</isbn><isbn>1424414830</isbn><isbn>1424414849</isbn><isbn>9781424414840</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kMtOwzAURM1LIpR8QTf5gZR7_YjtJYqgIBWBVJDYVTeNA0apU-Jk0b9vJMpsZjHS6MwwNkdYIIK9ey7v1-u3BQcwC6lQK6XP2A1KLiVKI-05S7jQNkcLnxcstdr8Z0JcsgQVh7xAaa9ZGuMPTJJKKKsSVr6M7eD3rcuaMdS0c2GgNmt69zu6sD1kLg5-R4PvQjZGH76yJY0xegpZ3HXd8B1cjLfsqqE2uvTkM_bx-PBePuWr1-VEvso95zDkhoqKOCkoDKlpAUJdCwmqdugMQVWgFkYgYDHRkzbFVjcSgFPV8KqiWszY_K_XO-c2-34C6w-b0x_iCIREUJU</recordid><startdate>20080101</startdate><enddate>20080101</enddate><creator>Pertusa, A.</creator><creator>Inesta, J.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20080101</creationdate><title>Multiple fundamental frequency estimation using Gaussian smoothness</title><author>Pertusa, A. ; Inesta, J.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i220t-8a6ba2a5068a555710dd3405de1e8a0b6173831016814a786c7f4002abf2bbad3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Acoustic applications</topic><topic>Acoustic measurements</topic><topic>acoustic signal analysis</topic><topic>Acoustic signal processing</topic><topic>Frequency estimation</topic><topic>Gaussian distributions</topic><topic>Harmonic analysis</topic><topic>Multiple signal classification</topic><topic>Music</topic><topic>Signal analysis</topic><topic>Signal generators</topic><topic>Spectral analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Pertusa, A.</creatorcontrib><creatorcontrib>Inesta, J.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pertusa, A.</au><au>Inesta, J.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multiple fundamental frequency estimation using Gaussian smoothness</atitle><btitle>2008 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2008-01-01</date><risdate>2008</risdate><spage>105</spage><epage>108</epage><pages>105-108</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424414833</isbn><isbn>1424414830</isbn><eisbn>1424414849</eisbn><eisbn>9781424414840</eisbn><abstract>The goal of a polyphonic music transcription system is to extract a score from an audio signal. A multiple fundamental frequency estimator is the main piece of these systems, whereas tempo detection and key estimation complement them to correctly extract the score. In this work, in order to detect the fundamental frequencies that are present in a signal, a set of candidates are selected from the spectrum, and all their possible combinations are generated. The best combination is chosen in a frame by frame analysis by applying a set of rules, taking into account the harmonic amplitudes and the spectral smoothness measure described in this work. The system was evaluated and compared to other works, yielding competitive results and performance.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2008.4517557</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1520-6149 |
| ispartof | 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, p.105-108 |
| issn | 1520-6149 2379-190X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Acoustic applications Acoustic measurements acoustic signal analysis Acoustic signal processing Frequency estimation Gaussian distributions Harmonic analysis Multiple signal classification Music Signal analysis Signal generators Spectral analysis |
| title | Multiple fundamental frequency estimation using Gaussian smoothness |
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