Details

Title

Music Mood Visualization Using Self-Organizing Maps

Journal title

Archives of Acoustics

Yearbook

2015

Volume

vol. 40

Numer

No 4

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Committee on Acoustics PAS, PAS Institute of Fundamental Technological Research, Polish Acoustical Society

Date

2015[2015.01.01 AD - 2015.12.31 AD]

Identifier

ISSN 0137-5075 ; eISSN 2300-262X

References

Plewa (2013), Multidimensional Scaling Analysis Applied to Music Mood Recognition Convention May Paper No, Audio Eng Soc, 4, 134. ; Kostek (2001), Representing Musical Instrument Sounds for Their Automatic Classification, Audio Eng Soc, 49, 768. ; Rumsey (2014), About Semantic Audio, Audio Eng Soc, 58, 281. ; Huron (2000), Perceptual and cognitive applications in music retrieval Conf MIR, Proc Int. ; Papaodysseus (2001), A New Approach to the Automatic Recognition of Musical Recordings, Audio Eng Soc, 46, 1. ; Kaminsky (1995), Automatic source identification of monophonic musical instrument sounds IEEE International Conference on Neural Networks, Proceedings. ; Drossos (2015), Evaluating the Impact of Sound Events Rhythm Characteristics to Listener s Valence, Audio Eng Soc, 63, 139, doi.org/10.17743/jaes.2015.0010 ; Lu (2006), Automatic mood detection and tracking of music audio signals Audio Speech Language Processing, IEEE Trans, 14, 5. ; Rumsey (2011), Semantic Audio : Machines Get Clever with Music, J Audio Eng Soc, 57, 882. ; Wagenaars (1986), Subjective Evaluation of Dynamic Compression in Music, J Audio Eng Soc, 66, 2. ; Kostek (2011), Content - Based Approach to Automatic Recommendation of Music st Convention New York, Audio Eng Soc October, 20, 131. ; Kostek (2013), Music Recommendation Based on Multidimensional Description and Similarity Measures, Fundamenta Informaticae, 127, doi.org/10.3233/FI-2013-912 ; Bigand (2005), Multidimensional scaling of emotional responses to music : The effect of musical expertise and of the duration of the excerpts, Cognition Emotion, 19, 1113, doi.org/10.1080/02699930500204250 ; Wieczorkowska (2011), Analysis of Recognition of a Musical Instrument in Sound Mixes Using Support Vector Machines, Fundamenta Informaticae, 67, 107. ; Markov (2014), Music Genre and Emotion Recognition Using Gaussian Processes, IEEE, 2, 2169. ; Novello (2011), Perceptual Evaluation of Inter - song Similarity in Western Popular Music New Music Research, J, 40, 1. ; Barbedo (2005), A New Cognitive Model for Objective Assessment of Audio Quality, Audio Eng Soc, 53, 1. ; Hevner (1936), Experimental studies of the elements of expression in music of, American Journal Psychology, 48, 246, doi.org/10.2307/1415746 ; Rauber (2002), Content - based Music Indexing and Organization Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Tampere, Proc, 50, 409. ; Panda (2011), Using Support Vector Machines for Automatic Mood Tracking in Audio Music th Convention Paper No London UK May, Audio Eng Soc, 13, 130. ; Casey (2008), Content - Based Music Information Retrieval : Current Directions and Future Challenges of the IEEE, Proc, 96. ; Kohonen (1984), Self Organized Formation of Topologically Correct Feature Maps, Biol Cybern, 19, 59. ; Lima (2012), A Multidimensional Scaling Analysis of Musical Sounds Based on Pseudo Phase Plane Special Issue Article ID, Appl Anal.

DOI

10.1515/aoa-2015-0051

×