3,094 research outputs found
Recognition of variations using automatic Schenkerian reduction.
Experiments on techniques to automatically recognise whether or not an extract of music is a variation of a given theme are reported, using a test corpus derived from ten of Mozart's sets of variations for piano. Methods which examine the notes of the 'surface' are compared with methods which make use of an automatically derived quasi-Schenkerian reduction of the theme and the extract in question. The maximum average F-measure achieved was 0.87. Unexpectedly, this was for a method of matching based on the surface alone, and in general the results for matches based on the surface were marginally better than those based on reduction, though the small number of possible test queries means that this result cannot be regarded as conclusive. Other inferences on which factors seem to be important in recognising variations are discussed. Possibilities for improved recognition of matching using reduction are outlined
Comparison Of Modified Dual Ternary Indexing And Multi-Key Hashing Algorithms For Music Information Retrieval
In this work we have compared two indexing algorithms that have been used to
index and retrieve Carnatic music songs. We have compared a modified algorithm
of the Dual ternary indexing algorithm for music indexing and retrieval with
the multi-key hashing indexing algorithm proposed by us. The modification in
the dual ternary algorithm was essential to handle variable length query phrase
and to accommodate features specific to Carnatic music. The dual ternary
indexing algorithm is adapted for Carnatic music by segmenting using the
segmentation technique for Carnatic music. The dual ternary algorithm is
compared with the multi-key hashing algorithm designed by us for indexing and
retrieval in which features like MFCC, spectral flux, melody string and
spectral centroid are used as features for indexing data into a hash table. The
way in which collision resolution was handled by this hash table is different
than the normal hash table approaches. It was observed that multi-key hashing
based retrieval had a lesser time complexity than dual-ternary based indexing
The algorithms were also compared for their precision and recall in which
multi-key hashing had a better recall than modified dual ternary indexing for
the sample data considered.Comment: 11 pages, 5 figure
IDENTIFICATION OF COVER SONGS USING INFORMATION THEORETIC MEASURES OF SIMILARITY
13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted versio
Music ranking techniques evaluated
In a music retrieval system, a user presents a piece of music as a query and the system must identify from a corpus of performances other pieces with a similar melody. Several techniques have been proposed for matching such queries to stored music. In previous work, we found that local alignment, a technique derived from bioinformatics, was more effective than the n-gram methods derived from information retrieval; other researchers have reported success with n-grams, but have not compared against local alignment. In this paper we explore a broader range of n-gram techniques, and test them with both manual queries and queries automatically extracted from MIDI files. Our experiments show that n-gram matching techniques can be as effective as local alignment; one highly effective technique is to simply count the number of n-grams in common between the query and the stored piece of music. N-grams are particularly effective for short queries and manual queries, while local alignment is superior for automatic queries
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system
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
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