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Incorporating user characteristics and contextual information has shown to be essential when it comes to personalized music retrieval and recommendation. To this end, the current location of a user is often exploited. However, relying solely on GPS coordinates neglects the cultural background of users, which does not necessarily coincide with political borders. In this paper, we analyze culture-specific...
Music Information Retrieval (MIR) focuses on retrieving useful information from collection of music. The objective of research work in this paper is to explore clustering approaches which can be useful in automatically mining the content from Carnatic instrumental music. The content to be retrieved is the instrument that is primarily used to play the song. Carnatic music songs with ten different instruments...
The relationship between music and physiological indicators of emotion has attracted increased attention in the past two decades. However, the limitations of small sample sizes and the difficulty in designing ecological studies involving physiology and emotion make it hard to gain convincing and practical insight into the mechanisms behind them. In this paper, we propose a novel combination of spectral...
We propose a feature level fusion that is based on mapping the original low-level audio features to histogram descriptors. Our mapping is based on possibilistic membership functions and has two main components. The first one consists of clustering each set of features and identifying a set of representative prototypes. The second component uses the learned prototypes within membership functions to...
To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox...
An advanced form of the Partitioned Feature-based Classifier (PFC) is proposed in this paper. As is the case with the PFC, the proposed classifier model, called Partitioned Feature-based Classifier with Expertise Table (PFC-ET), does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each...
Although multiple music recordings may sound identical to a human listener, the underlying representations of sound may differ due to the variations in their audio encoding and/or transmission methods. In contrast to the existing audio-fingerprinting techniques, which establishes the fingerprint of each source music to identify unknown, ldquodistortedrdquo audio clips, this paper proposes an unsupervised...
Clustering for better representation of the diversity of text or image search results has been studied extensively. In this paper, we extend this methodology to the novel domain of music search. We conduct empirical evaluation of different clustering algorithms, audio feature representations, and the incorporation of lyrics for music clustering. Our evaluation shows the fusion of audio and text features...
This paper presents the system for automatic emotion detection from music data stored in MIDI format files. First, the piece of music is divided into independent segments that potentially represent different emotional states. For this task the method of segmentation is used. The most important part is a features extraction from the music data. On this basis similar emotional parts are grouped by clustering...
A user interface to music repositories called nepTune creates a virtual landscape for an arbitrary collection of digital music files, letting users freely navigate the collection. Automatically extracting features from the audio signal and clustering the music pieces accomplish this. The clustering helps generate a 3D island landscape. The rapidly growing research field of music information retrieval...
In this paper, we present effective methods for music summarization which automatically extract a representative portion of the music by signal processing technology. Our proposed method uses 2-dimensional similarity matrix, tempo tracking, and clustering techniques to extract several segments which have different moods or dissimilar semantic structure in the music. The segments extracted are combined...
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