Co-occurrence Models in Music Genre Classification

Music genre classification has been investigated using many different methods, but most of them build on probabilistic models of feature vectors xr which only represent the short time segment with index r of the song. Here, three different co-occurrence models are proposed which instead consider the whole song as an integrated part of the probabilistic model. This was achieved by considering a song as a set of independent co-occurrences (s, xr) (s is the song index) instead of just a set of independent (xr)’s. The models were tested against two baseline classification methods on a difficult 11 genre data set with a variety of modern music. The basis was a so-called AR feature representation of the music. Besides the benefit of having proper probabilistic models of the whole song, the lowest classification test errors were found using one of the proposed models. 1

Integration over song classification replicates: Song variant analysis in the hihi

Human expert analyses are commonly used in bioacoustic studies and can potentially limit the reproducibility of these results. In this paper, a machine learning method is presented to statistically classify avian vocalizations. Automated approaches were applied to isolate bird songs from long field recordings, assess song similarities, and classify songs into distinct variants. Because no positive controls were available to assess the true classification of variants, multiple replicates of automatic classification of song variants were analyzed to investigate clustering uncertainty. The automatic classifications were more similar to the expert classifications than expected by chance. Application of these methods demonstrated the presence of discrete song variants in an island population of the New Zealand hihi (Notiomystis cincta). The geographic patterns of song variation were then revealed by integrating over classification replicates. Because this automated approach considers variation in song variant classification, it reduces potential human bias and facilitates the reproducibility of the results

Non-song vocalizations of humpback whales in Western Australia

This study presents non-song vocalizations of humpback whales (Megaptera novaeangliae) from two migratory areas off the Western Australian coast: Geographe Bay and Port Hedland. A total of 220 sounds were identified as non-song sounds in 193 h of recordings reviewed. Of those, 68 were measured and qualitatively classified into 17 groups using their spectral features. One group (HW-02) had a high level of variation in terms of spectral slope. However, further classification using statistical classification methods was not possible because of the small sample size. Non-song sound frequencies varied from 9 Hz to 6 kHz, with the majority of sounds under 200 Hz. The duration of non-song sounds varied between 0.09 and 3.59 s. Overall, the use of spectral features allowed general classification of humpback whale sounds in a low sample size scenario that was not conducive to using quantitative methods. However, for highly variable groups, quantitative statistical classification methods (e.g., random forests) are needed to improve classification accuracy. The identification and accurate classification of a species’ acoustic repertoire is key to effectively monitor population status using acoustic techniques and to better understand the vocal behavior of the species. The results of this study improve the monitoring of humpback whales by standardizing the classification of sounds and including them in the species’ repertoire. The inclusion of non-song sounds in passive acoustic monitoring of humpback whales will add females and calves to the detection counts of otherwise only singing males. © Copyright © 2020 Recalde-Salas, Erbe, Salgado Kent and Parsons

Music Similarity Estimation

Music is a complicated form of communication, where creators and culture communicate and expose their individuality. After music digitalization took place, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). To build these systems and recommend the right choice of song to the user, classification of songs is required. In this paper, we propose an approach for finding similarity between music based on mid-level attributes like pitch, midi value corresponding to pitch, interval, contour and duration and applying text based classification techniques. Our system predicts jazz, metal and ragtime for western music. The experiment to predict the genre of music is conducted based on 450 music files and maximum accuracy achieved is 95.8% across different n-grams. We have also analyzed the Indian classical Carnatic music and are classifying them based on its raga. Our system predicts Sankarabharam, Mohanam and Sindhubhairavi ragas. The experiment to predict the raga of the song is conducted based on 95 music files and the maximum accuracy achieved is 90.3% across different n-grams. Performance evaluation is done by using the accuracy score of scikit-learn

Struktur dan Klasifikasi Pantun dalam Lirik Lagu Daerah Dayak Kanayatn

The main issue discussed in this research is How is structure and classification of pantun in Dayak Kanayatn song lyrics? This study aims to describe about rhyme, meaning, and classification of pantun Dayak Kanayatn song lyrics. The research methodology applied in this research is descriptive method, while the form is qualitative research with structural approach. The data acquisition was done with documentary study technique on a collection of Dayak Kanayatn song videotape. Devices which are used in this research are VCD player, computer, writing tools, and the note. The result of the research is as follow. The structure of pantun in Dayak Kanayatn song lyrics includes (1) assonance rhyme, (2) alliteration rhyme, (3) final rhyme, (4) inner rhyme, (5) identical rhyme, (6) rhyme of form, and (7) full rhyme. Meaning of pantun includes (1) joy expression, (2) sorrow expression, (3) love expression, and (4) proverb expression. The classification of pantun is based on two aspects, i.e. forms and themes

Struktur dan Klasifikasi Pantun dalam Lirik Lagu Daerah Dayak Kanayatn

The main issue discussed in this research is How is structure and classification of pantun in Dayak Kanayatn song lyrics? This study aims to describe about rhyme, meaning, and classification of pantun Dayak Kanayatn song lyrics. The research methodology applied in this research is descriptive method, while the form is qualitative research with structural approach. The data acquisition was done with documentary study technique on a collection of Dayak Kanayatn song videotape. Devices which are used in this research are VCD player, computer, writing tools, and the note. The result of the research is as follow. The structure of pantun in Dayak Kanayatn song lyrics includes (1) assonance rhyme, (2) alliteration rhyme, (3) final rhyme, (4) inner rhyme, (5) identical rhyme, (6) rhyme of form, and (7) full rhyme. Meaning of pantun includes (1) joy expression, (2) sorrow expression, (3) love expression, and (4) proverb expression. The classification of pantun is based on two aspects, i.e. forms and themes