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Ninad, Journal of the ITC-SRA, vol.20 December 2006: Abstracts

MEASUREMENT OF IMPACT OF MUSIC ON THE HUMAN MIND USING C-MONITOR - G. C. Ray & Gautam Das

Abstract
The paper presents the details of design of C-Monitor for the direct measurement of change in level of consciousness of a human subject . The reason for the change in the level of consciousness may be music, meditation, pleasure or pain or simple anaesthetic drugs during surgical operations. The input to the device is the ECG (electrocardiographic signal) and the hardware-cum-software combination extracts respiratory sinus arrhythmia (RSA) which varies in proportion to the level of consciousness.

Apart from the design of the instrument, its use in surgical operations with almost all commonly used anaesthetic drugs as well as the use in pain-monitoring are described. Experimental results are presented to show the level of accuracy of the measurements. Finally its use is shown in the field of music. The effect of conscious -attention causes a change in the magnitude of high gamma (70 – 150 Hz), as is known recently, and this is correlated with the change in RSA due to the impact of music.

OBJECTIVE ANALYSIS OF THE INTERVAL BOUNDARIES AND SWARA-SHRUTI RELATIONS IN HINDUSTANI VOCAL MUSIC FROM ACTUAL PERFORMANCES - A. K. Datta, R. Sengupta, N. Dey, D Nag & A Mukerjee

Abstract
A precise definition of shrutis has remained ambiguous even till modern times. The position of the ratios for different shrutis with respect to the shruti intervals, i.e. whether the ratio indicates the beginning or end or the middle of an interval, is not quite clear. The ancient texts suggest that the number of shrutis holding swaras is not equal for all swaras. There are four shrutis each on shadaj, madhyama and panchama, two each for gandhar and nishada and three each for rishabha and dhaivata.

Therefore, to address these particular questions by an objective analysis, the song signals of a number of established singers of Hindustani music have been analysed. For the analysis, 116 songs on four ragas namely Bhairav (Thaat –Bhairav), Darbari Kanhara (Thaat – Asavari), Mian-ki-Malhar (Thaat – Kafi) and Todi (Thaat – Todi), sung by twenty three (23) singers from five different gharanas of Hindustani music, namely Agra, Kirana, Patiala, Gwalior and Mixed have been taken for analysis. Pitch data from 116 songs on four ragas namely Bhairav, Darbari Kanhara, Mian-ki-Malhar and Todi, sung by twenty-three (23) singers conforms strongly to: Shruti distribution of 1, 4, 4, 4, 1, 4 and 4 respectively for swaras Sa, Re, Ga, Ma, Pa, Dha and Ni.

Interval boundary being at the middle, particularly at geometric mean of the interval not at any end.

OVERVIEW OF PROFESSIONAL VOICE CARE - S.R.Savithri

Abstract
The article is an overview of professional voice care. It provides the physiological and anatomical pre-requisites for professional voice users. It also reviews information on classification of voice, pathologies affecting vocal folds in professional voice users and provides recent information on management of professional voice users.

NADIA TU DHIRE BAHO RE : FILM SOUNDTRACK AS A MUSICAL PORTRAIT - Amelia Maciszewski

Abstract
This article examines a documentary film made on Girija Devi, exploring the reasons that prompted its making, the format used and the music that was incorporated into the film's sound track, thus bringing out the essence of the artiste's personality.

AUTOMATIC RAGA CLASSIFICATION OF SAROD AND VOCAL PERFORMANCES USING PITCH-CLASS AND PITCH-CLASS DYAD DISTRIBUTIONS - Parag Chordia

Abstract
A system was constructed to automatically identify ragas using pitch-class (PCDs) and pitch-class dyad distributions (PCDDs) derived from pitch-tracked performances. Classification performance was 93% in a 10-fold cross-validation test with 17 target ragas drawn from a database containing 232 minutes of recorded performances. Best performance was attained using a maximum a posteriori (MAP) rule with a multivariate normal (MVN) likelihood model. Each raga was divided into non-overlapping 30 second segments and pitch tracked using the Harmonic Product Spectrum (HPS) algorithm. Pitch tracks were then transformed into pitchclass sequences by segmenting into notes using a complexdomain detection function. For each note, pitch-class was determined by taking the mode of the detected pitches from the onset of the note to the next onset. For the given tuning, the nearest pitch was found based on a just-intoned chromatic scale. PCDs and PCDDs were estimated from each segment leading to 12 PCD features and 144 PCDD features, representing the relative frequency of pitch-classes and pitch dyads respectively. The study suggests that PCDs and PCDDs may be effective features for raga classification. However, the database size must be expanded in size and diversity to confirm this more generally.

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