Musical Genre Classication
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1 Musical Genre Classication Jan Müllers RWTH Aachen, 2015 Jan Müllers Finding Disjoint Paths 1 / 15
2 Musical Genres The Problem Musical Genres History Automatic Speech Regocnition categorical labels created by humans to characterize pieces of music caracterized by the instrumentation, rhythmic structure, and harmonic content used to structure large collections of music Jan Müllers Finding Disjoint Paths 2 / 15
3 Automatic Genre Classication Musical Genres History Automatic Speech Regocnition manual genre classication is slow and expensive automatic genre classication can assist or replace the human user Jan Müllers Finding Disjoint Paths 3 / 15
4 Limitations The Problem Musical Genres History Automatic Speech Regocnition only small size of dierent genres (10 or less) only hard decision problem (only one label for a song) maybe not close to praxis (over 500 dierent genres, songs can belong to dierent gernres, subgernres) Jan Müllers Finding Disjoint Paths 4 / 15
5 History The Problem Musical Genres History Automatic Speech Regocnition rst ideas: 15 years ago when large digital music libarys got common rst approach: using ideas from automatic speech recognition [1] each paper after that used new features and dierent classication methods to increase the accuracy Jan Müllers Finding Disjoint Paths 5 / 15
6 Automatic Speech Regocnition Musical Genres History Automatic Speech Regocnition already researched since 1970s similar problem: also applying a class to an audio signal (word/sentence <-> genre) both need a way to represent the audio signal as an feature-vector no context needed for genre classication Jan Müllers Finding Disjoint Paths 6 / 15
7 2 Problems Features 2 Steps The Problem can be divided into 2 diernet parts: transforming the audio-signal into a feature vector classication of the vector for step 2 one can use standart classication methods, research: which is the best? for step 1 new features can be developed and tested Jan Müllers Finding Disjoint Paths 7 / 15
8 Features that can be used 2 Problems Features Dierent papers introduced features that can be used: Mel Frequency Cepstral Coecients (as used in Speech Regocnition) melody rhythm pitch Jan Müllers Finding Disjoint Paths 8 / 15
9 Locality Preserving Non-Negative Tensor Factorization Results Locality Preserving Non-Negative Tensor Factorization a state of the art approach with low error-rates introduced by Yannis Panagakis, Constantine Kotropoulos and Gonzalo R. Arce in 2009 [2] Jan Müllers Finding Disjoint Paths 9 / 15
10 The Idea The Problem Locality Preserving Non-Negative Tensor Factorization Results the rst part is the LPNTF the second part is Sparse Representation-Based Classication Jan Müllers Finding Disjoint Paths 10 / 15
11 How LPNTF works The Problem Locality Preserving Non-Negative Tensor Factorization Results tensor: multidimensional equivalent of matrices and vectors non-negative: all tensors have no negative elements factorization: a tensor is divided in several vectors, which linear combined give the tensor locality preserving: take the nearest neighbor graph into acount Jan Müllers Finding Disjoint Paths 11 / 15
12 Locality Preserving Non-Negative Tensor Factorization Results Sparse Representation-Based Classication a classication method rst introduced for automatic face regocnition idea: we have an dictionary created in training we presented a song as a linear combination of atoms from the dictionary which all belong to one genre Jan Müllers Finding Disjoint Paths 12 / 15
13 Results The Problem Locality Preserving Non-Negative Tensor Factorization Results Errorrates are around 95% with this approach not all papers are comparable due to dierent test settings (number of genres, dierent databases) Jan Müllers Finding Disjoint Paths 13 / 15
14 Two steps: transforming audio-signal in feature vector classing feature vector still low number of dierent genres No replacement for human experts yet, can only assist Jan Müllers Finding Disjoint Paths 14 / 15
15 References References I George Tzanetakis, Perry Cook. Musical Genre Classication of Audio Signals. IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 10, NO. 5, JULY Yannis Panagakis, Constantine Kotropoulos, Gonzalo R. Arce. MUSIC GENRE CLASSIFICATION USING LOCALITY PRESERVING NON-NEGATIVE TENSOR FACTORIZATION AND SPARSE REPRESENTATIONS. 10th International Society for Music Information Retrieval Conference (ISMIR 2009). Jan Müllers Finding Disjoint Paths 15 / 15
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