Music Information Retrieval from a Singing Voice Using Lyrics and Melody Information

This study was conducted at the Music Library of the Ministry of Information (ML-MOI) and the Music Library of the Cultural Affairs (ML-CA) of Eritrea. The main aim of the study was to designan appropriate Indigenous Music Information Storage and Retrieval System for Eritrea. A quantitative approach was mainly used to obtain data from a purposefully selected sample. The qualitative approach was also used in some research stages. Methods used included documentanalysis, questionnaire, personal interview, focus group discussion and observation. It was found that music data was recorded in a register, which also served as a finding aid. Users’ preference was to search for music by artist, title, producer, occasion, subject or language.Unfortunately this was not possible with the system that was being used since it only allowed searching through the name or initials of an artist. The paper recommends the establishment of an automated indigenous storage and retrieval system to enable registration of indigenous knowledge information, offering facilities for indexing, searching and report generation.

This paper introduces the Raga Based Music Classification as a part of Music Information Retrieval (MIR) systems for retrieving musical information. The music has been more than just a sound that entertains us. MIR users like musicology researchers, musicians, music learners, music therapists, etc., require not just the sound but the actual strength of the music that controls our body, motivates us, cures mental illness, etc. The backbone of any form of music is its raga. A raga is a particular combination of notes with certain laws which, when carefully observed, preserves and safeguards their integrity and create wonders in music. The existing MIR systems and audio search engines concentrate on content based audio mining and not ragas. This paper discusses the classification of ragas and how it differs in Carnatic, Hindustani and Western musical forms. It also describes the various features that can be extracted from an audio data to represent a raga. Finally, we propose a system architecture to perform raga based classification of music.

Raga is a soul of Indian Music and the Indian Music has been studied on the basis of Raga. It can also be the main parameter for content based retrieval of audio in Music Information Retrieval(MIR) system. In Western music, melody has not many variations, but in Indian music melody has many variations in the form of ragas. Difficulty in understanding the complex structure of raga makes automatic Raga Identification problem a critical one. Many different attempts have been made to address the raga identification problem but realization of such a system for a large collection of raga is a long way to go. There are many issues in regard to this that need to be solved. We have designed solution to tackle two issues in our work and present the methods and the experimental analysis in this paper. The first issue is to handle the large number of raga classes which is solved through the clustering technique based on jump sequence. The second issue that is addressed here is avoiding the calculation of the scale (tonic) of the song to identify the notes in the song. So approaching a method where raga identification can be done even without knowing tonic (scale) of the song. We present the importance of jump sequence in raga identification in this paper. We also present the experimental results on naturally clustering the ragas along with cluster identification for a given song.

Music information retrieval (MIR) systems tend to fall into two camps: that camp developing cataloging and providing advanced access systems for large collections of music and that camp developing specific query or access mechanisms. We have started to merge these camps by integrating Variations2, which provides access to a digitized portion of Indiana University's vast music library, with Michigan's VocalSearch, which provides a query-by-humming (QBH) search engine. The joint system, V2V, demonstrates how QBH can be used in connection with a large number of holdings in a real-world environment.

The main objective of the project is to build a system that allows users to predict genre as well as language out of some music . The Automated determination of musical genre and linguistic categorization is a machine learning based prediction model which is used to predict the genres and languages of an audio . Musical genres are categorical labels created by humans to characterize piece of music. A musical genre is characterized by common characteristics typically related to the instrumentation, rhythmic structure and harmonic content of the music. The performance and relative importance of the proposed features are investigated by training the statistical pattern recognition classifiers using real world audio collections and using machine learning algorithm.In this project, we built systems called music genre and language classification and the effects of different feature extraction methods and their various combinations were also investigated. This task is of great interest in music information retrieval and recommendation systems. In recent years, several machine learning algorithms have been developed to tackle this task, using features such spectral, temporal, and tonal characteristics of audio signals. Music genre prediction and language classification is a task that involves automatically predicting a piece of music into a specific genre category as well as language based on its audio features. Keywords-machine-learning,music,music genre and language classification

Quantification of stylistic differences between musical artists is of academic interest to the music community, and is also useful for other applications such as music information retrieval and recommendation systems. Information about stylistic differences can be obtained by comparing the performances of different artists across common musical pieces. In this article, we develop a statistical methodology for identifying and quantifying systematic stylistic differences among artists that are consistent across audio recordings of a common set of pieces, in terms of several musical features. Our focus is on a comparison of ten different orchestras, based on data from audio recordings of the nine Beethoven symphonies. As generative or fully parametric models of raw audio data can be highly complex, and more complex than necessary for our goal of identifying differences between orchestras, we propose to reduce the data from a set of audio recordings down to pairwise distances between orchestras, based on different musical characteristics of the recordings, such as tempo, dynamics, and timbre. For each of these characteristics, we obtain multiple pairwise distance matrices, one for each movement of each symphony. We develop a hierarchical multidimensional scaling (HMDS) model to identify and quantify systematic differences between orchestras in terms of these three musical characteristics, and interpret the results in the context of known qualitative information about the orchestras. This methodology is able to recover several expected systematic similarities between orchestras, as well as to identify some more novel results. For example, we find that modern recordings exhibit a high degree of similarity to each other, as compared to older recordings.

Combining evidences from magnitude and phase information using VTEO for person recognition using humming

Music Recognition System has becoming popular these days. They are based on either audio or symbolic method, which compares the user's query with the existing music database. The investigation has shown that the audio-based method system is good for storing sound waves. However, the limitation is to illustrate the content of the music. The symbolic-based method system doing well in representing the content of the music, including recognize similar patterns, but limitation in creating the music, e.g., Electronic Music. We also carried some detailed tests based on the previous Neural Network systems, with Music Definition Language (MDL) and Music Manipulation Language (MML). Furthermore, we have tested our previous classification system with new melody query, to see how it can handle with external music pieces, which beyond the self-testing from a self-organising-map. The conclusion is that our system can classify variation type, including key variations, expansion and reduction, which is better than those existing Music Information Retrieval (MIR) systems.

Current Music Information Retrieval (MIR) systems focus on melody based retrieval with some tolerance for user errors in the melody specification. The system described here presents a novel method for theme retrieval: A theme is described as a list of musical events, containing melody and harmony features, which must be presented in a given order and within a given time frame. The system retrieves musical phrases that fit the description. A system of this type could serve musicians and listeners who wish to discover thematically similar phrases in music digital libraries. The prototype and underlying model have been tested on midi sequences of music by W.A. Mozart and have shown good performance results.

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years.

This paper investigates the generic problem of model selection in the specific context of Music Information Retrieval (MIR). In MIR research, similarity measures are developed for ranking musical items with respect to their relevance to a user's musical query. The application of such similarity measures in MIR systems typically requires musical works to be divided into more manageable units. This involves two tasks: melody segmentation and voice separation. For both of these tasks, several computational models have been proposed in the symbolic domain. It seems reasonable to assume that those solutions that are most in accordance with human performance will result in the best ranking of retrieval output. We conducted two experiments, each with twenty experts and twenty novices. In the melody segmentation experiment, we found a high agreement between the participants. Evaluating algorithm output against participant data, we conclude that human output cannot be distinguished from three of the segmentation algorithms (Grouper, IDyOM and LBDM). For voice separation—which we evaluated by means of a melody identification task—the situation is different, as the combined results of two algorithms (Skyline and SSA) were shown to agree best with experimental results, and differences were found between novice and expert performance. Several other model selection criteria besides performance are discussed in conclusion.

Finite state automata are of fundamental importance in theory as well as applications in computer science. This dissertation presents several parallel algorithms for processing of them, including minimizing the number of states for a given finite state automaton, finding homing sequences for a given finite state automaton, converting a regular expression into an $\\varepsilon$-free nondeterministic finite automaton. Implementation of some algorithms, as well as their experimental results, are also given. For minimization of deterministic finite automata, we present a parallel algorithm and discuss its implementation on a practical parallel machine using data parallel and message passing paradigms. Its time complexity on a p processor EREW PRAM $(p\\le n)$ for inputs of size n is $O({n\\log\\sp2n\\over p}+\\log n\\ \\log p)$ uniformly on almost all instances. The work done by our algorithm is thus within a factor of $O(\\log n)$ of the best known sequential algorithm. The space used by our algorithm is linear in the input size. The actual resource requirements of our implementations are consistent with these estimates. For finding homing sequences of deterministic finite automata, we present both deterministic and randomized algorithms. Two randomized algorithms of time complexity $O(log\\sp2n)$ are given. One of the randomized algorithms produces a homing sequence of length $O(n\\ log\\sp2n)$ for almost all DFA's with n states. The deterministic algorithm is based on a well known sequential algorithm. The implementation of the deterministic parallel algorithm shows that it can handle randomly generated instances with millions of states, and all DFA's with thousands of states using currently available parallel machines. Finally, we present an efficient parallel algorithm for converting a regular expression into an $\\varepsilon$-free nondeterministic finite automaton. The resulting automaton is natural representation of a regular expression and has some important and practical uses in the document processing area. The time complexity of the algorithm is $O(\\log n)$ and the number of processors used is $O(n\\sp2).$

Currents estimates put canon of traditional Irish dance tunes at least 7,000 compositions. Given this diversity, a common problem faced by musicians and ethnomusicologists is identifying tunes from recordings. This is evident even in number of commercial recordings whose title is gan ainm (without name). This work attempts to solve this problem by developing a Content Based Music Information Retrieval (CBMIR) System adapted to characteristics of traditional Irish music. A system is presented called MATT2 (Machine Annotation of Traditional Tunes) whose primary goal is to annotate recordings of traditional Irish dance music with useful meta-data including tune names. MATT2 incorporates a number of novel algorithms for transcription of traditional music and for adapting melodic similarity measures to creativity and style present in playing of traditional music. It incorporates a new algorithm for filtering ornamentation notes and accommodating the long note in traditional music called Ornamentation Filtering using Adaptive Histograms (OFAH). A new algorithm is presented called TANSEY (Turn ANnotation from SEts using SimilaritY profiles) that annotates sets of tunes played segue as is custom in traditional Irish dance music. The work presented is validated in experiments using 130 real-world field recordings of traditional music from sessions, classes, concerts and commercial recordings. Test audio includes solo and ensemble playing on a variety of instruments recorded in real-world settings such as noisy public sessions. Results are reported using standard measure from field of information retrieval (IR) including accuracy, error, precision and recall and system is compared to alternative approaches for CBMIR common in literature.

The article describes the project on music information retrieval that has been carried out at the University of Padova, Italy. The research work has been characterized by the synergy of the modular integration of sound techniques of melody processing and of statistical information retrieval. After illustrating the background from which the project has originated, we describe the complete process, from methodology design through evaluation and system implementation. Conclusions, impacts on research in music information retrieval, and future directions are also described.

Parallel communicating finite automata (PCFAs) are systems of several finite state automata which process a common input string in a parallel way and are able to communicate by sending their states upon request. We consider deterministic and nondeterministic variants and distinguish four working modes. It is known that these systems in the most general mode are as powerful as one-way multihead finite automata. It is additionally known that the number of heads corresponds to the number of automata in PCFAs in a constructive way. Thus, undecidability results as well as results on the hierarchies induced by the number of heads carry over from multi-head finite automata to PCFAs in the most general mode. Here, we complement these undecidability and hierarchy results also for the remaining working modes. In particular, we show that classical decidability questions are not semi-decidable for any type of PCFAs under consideration. Moreover, it is proven that the number of automata in the system induces infinite hierarchies for deterministic and nondeterministic PCFAs in three working modes.