Vignesh Ishwar

Classification of Melodic Motifs in Raga Music with Time-series Matching

Abstract Ragas are characterized by their melodic motifs or catch phrases that constitute strong cues to the raga identity for both the performer and the listener, and therefore are of great interest in music retrieval and automatic transcription. While the characteristic phrases, or pakads, appear in written notation as a sequence of notes, musicological rules for interpretation of the phrase in performance in a manner that allows considerable creative expression, while not transgressing raga grammar, are not explicitly defined. In this work, machine learning methods are used on labelled databases of Hindustani and Carnatic vocal audio concerts to obtain phrase classification on manually segmented audio. Dynamic time warping and HMM based classification are applied on time series of detected pitch values used for the melodic representation of a phrase. Retrieval experiments on raga-characteristic phrases show promising results while providing interesting insights on the nature of variation in the surface realization of raga-characteristic motifs within and across concerts.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

Abstract The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rāg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rāg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

Intonation Analysis of Rāgas in Carnatic Music

Abstract Intonation is a fundamental music concept that has a special relevance in Indian art music. It is characteristic of a rga and key to the musical expression of the artist. Describing intonation is of importance to several music information retrieval tasks such as developing similarity measures based on rgas and artists. In this paper, we first assess rga intonation qualitatively by analysing varṇaṁs, a particular form of Carnatic music compositions. We then approach the task of automatically obtaining a compact representation of the intonation of a recording from its pitch track. We propose two approaches based on the parametrization of pitch-value distributions: performance pitch histograms, and context-based svara distributions obtained by categorizing pitch contours based on the melodic context. We evaluate both approaches on a large Carnatic music collection and discuss their merits and limitations. We finally go through different kinds of contextual information that can be obtained to further improve the two approaches.

Mining Melodic Patterns in Large Audio Collections of Indian Art Music

Discovery of repeating structures in music is fundamental to its analysis, understanding and interpretation. We present a data-driven approach for the discovery of short-time melodic patterns in large collections of Indian art music. The approach first discovers melodic patterns within an audio recording and subsequently searches for their repetitions in the entire music collection. We compute similarity between melodic patterns using dynamic time warping (DTW). Furthermore, we investigate four different variants of the DTW cost function for rank refinement of the obtained results. The music collection used in this study comprises 1,764 audio recordings with a total duration of 365 hours. Over 13 trillion DTW distance computations are done for the entire dataset. Due to the computational complexity of the task, different lower bounding and early abandoning techniques are applied during DTW distance computation. An evaluation based on expert feedback on a subset of the dataset shows that the discovered melodic patterns are musically relevant. Several musically interesting relationships are discovered, yielding further scope for establishing novel similarity measures based on melodic patterns. The discovered melodic patterns can further be used in challenging computational tasks such as automatic raga recognition, composition identification and music recommendation.

Phrase-based rĀga recognition using vector space modeling

Automatic raga recognition is one of the fundamental computational tasks in Indian art music. Motivated by the way seasoned listeners identify ragas, we propose a raga recognition approach based on melodic phrases. Firstly, we extract melodic patterns from a collection of audio recordings in an unsupervised way. Next, we group similar patterns by exploiting complex networks concepts and techniques. Drawing an analogy to topic modeling in text classification, we then represent audio recordings using a vector space model. Finally, we employ a number of classification strategies to build a predictive model for raga recognition. To evaluate our approach, we compile a music collection of over 124 hours, comprising 480 recordings and 40 ragas. We obtain 70% accuracy with the full 40-raga collection, and up to 92% accuracy with its 10-raga subset. We show that phrase-based raga recognition is a successful strategy, on par with the state of the art, and sometimes outperforms it. A by-product of our approach, which arguably is as important as the task of raga recognition, is the identification of raga-phrases. These phrases can be used as a dictionary of semantically-meaningful melodic units for several computational tasks in Indian art music.

Discovering rāga motifs by characterizing communities in networks of melodic patterns

Ra̅ga motifs are the main building blocks of the melodic structures in Indian art music. Therefore, the discovery and characterization of such motifs is fundamental for the computational analysis of this music. We propose an approach for discovering ra̅ga motifs from audio music collections. First, we extract melodic patterns from a collection of 44 hours of audio comprising 160 recordings belonging to 10 ra̅gas. Next, we characterize these patterns by performing a network analysis, detecting non-overlapping communities, and exploiting the topological properties of the network to determine a similarity threshold. With that, we select a number of motif candidates that are representative of a ra̅ga, the ra̅ga motifs. For a formal evaluation we perform listening tests with 10 professional musicians. The results indicate that, on an average, the selected melodic phrases correspond to ra̅ga motifs with 85% positive ratings. This opens up the possibilities for many musically-meaningful computational tasks in Indian art music, including human-interpretable ra̅ga recognition, semantic-based music discovery, or pedagogical tools.