Overview of Automatic Indian Music Information Recognition, Classification and Retrieval Systems Trisiladevi C. Nagavi Nagappa U. Bhajantri Department of Computer Science and Engineering Department of Computer Science and Engineering S J College of Engineering Government Engineering College Mysore, Karnataka, India Chamarajanagar, Karnataka, India tnagavi@yahoo.com bhajan3nu@gmail.com Abstract- In recent time conventional ways of listening to music, and methods for discovering music, such as radio broadcasts and record stores, are being replaced by personalized ways to hear and learn about music. There is abundant research and experimentation done over Western music. However, moderate amount of work noticed over Indian music and its related fields such as computational musicology and artificial intelligence to the realm of Indian music [1]. On the other hand, Indian music has adequate history and slowly it is becoming an international phenomenon. This has opened a wide opportunity for an Indian music discovery system which can suggest music based either on known artists or on simple descriptive terms. Hence researchers from various domains such as music processing, computer engineering and artificial intelligence etc. are contributing more towards Indian music processing. Eventually Indian music is broadly classified into South Indian Carnatic music and North Indian Hindustani music. Both the systems of music are rich in their own style and Carnatic music is much more complex in the way the notes are arranged and rendered [11]. Apart from these two broader categories, there are various types of Indian music. They are folk, tribal, bhajans or devotional, bhangra, Indi-pop, film songs, fusion and ghazals. This paper presents an overview of previous works on automatic Indian music information recognition, classification and retrieval. Furthermore the comparative study of the recognition, classification and retrieval techniques effectiveness based on various factors is also presented. Key words: Hidden Markov Models, Music Information Retrieval, Artificial Neural Networks, Pitch Class Distribution and Bayesian Decision Rule. I. INTRODUCTION Content based online music enabling systems are being developed and they are based on melody features. Melody can be defined as a series of notes sounding in succession. Also it is a sequence of pitches and durations. The basic elements of a melody are pitch, duration, dynamics and timbre, where pitch is perceived fundamental frequency, and duration is particular time interval. Further dynamics refers to softness or loudness of a sound and timbre which is also called as tone color is the quality or sound of a voice or instrument. Coming to online music access, we can say Music Information Retrieval (MIR) is the interdisciplinary field of retrieving information from music. In many publications [5, 6, 7, 8, 14] of content-based MIR, musical concepts such as melody or harmony, similarity measures between melodies, and finding occurrences and variations of a musical structure within a melody are used. Eventually, due to the encouragement of ardent music lover, uncountable numbers of people have contributed for the growth of Indian music in a larger extent. The main concentration of Indian music researchers is automatic classification of ragas which has lead to a rich debate on the essential characteristics of raga and the features that make two ragas similar or dissimilar [3]. Raga is defined as a characteristic arrangement or progression of notes whose full potential and complexity can only be realized in exposition. It is important to note here that no two performances of the same raga, even two performances by the same artist, will be identical [2]. The majority of research contributions fall into two broader categories in order to use Indian music information from MIR. The first category models music as linear strings and the second one models music as sets of two-dimensional geometric objects. After elucidation of melody, MIR and Indian music, we investigate some of research contributions dealing with identification of raga, swara etc. from a collection of music recorded as Musical Instrument Digital Interface (MIDI), MP3 and audio files. Our paper has attempted to portray the overview of automatic Indian music information systems using different feature extraction algorithms and classifiers. Subsequently in section II effectiveness of the recognition, classification and retrieval techniques are being compared based on various factors are comprehended. Section III is a discussion and result analysis of previous works. Conclusion of the paper is presented in section IV. II. AUTOMATIC INDIAN MUSIC INFORMATION SYSTEMS The specialization of MIR for Indian Music is considered in comprehensive publications covered by research community. Majority [4, 6, 7, 12, 14] of content-based MIR are engaged in intelligent, automated processing of music. Many of them have addressed some of the recent developments in content-based analysis and retrieval of music, they paid particular attention to the methods by which important information about music signals and symbols can be automatically extracted and processed for MIR systems. Automatic music information system is an ideal example of multidisciplinary research. Nowadays, there are a large number of research works focused on the automatic music information recognition, classification and retrieval by methods of acoustic analysis, feature extraction, neural network and statistics. This section presents an overview of previous works found in the literature which concentrates on how the automatic music 2011 International Conference on Recent Trends in Information Systems 978-1-4577-0792-6/11/$26.00 ©2011 IEEE 111