Monophonic Research Articles

Bounds for monophonic domination sum of a graph and its complement - A Nordhaus-Gaddum approach

Bounds for monophonic domination sum of a graph and its complement - A Nordhaus-Gaddum approach

P22.04: Fetal speech movement response comparing the intravaginal emission of monophonic and polyphonic music

P22.04: Fetal speech movement response comparing the intravaginal emission of monophonic and polyphonic music

CALCULATION OF PITCH FOR THE IRANIAN TRADITIONAL MUSIC GENRE CLASSIFICATION STUDIED ON TWO STRINGED INSTRUMENTS THE TAR AND THE SETAR

Audio signal classification system analyzes the input audio signal and creates a label that describes the signal at the output. These are used to characterize both music and speech signals. The categorization can be done on the basis of pitch, music content, music tempo and rhythm. A musical sound is said to have four perceptual attributes: pitch, loudness, duration and timbre. These four attributes make it possible for a listener to distinguish musical sounds from each other. Pitch is a perceptual and subjective attribute of sound and plays an important role in the human understanding of sound. Although the pitch estimation of monophonic sound is well resolved, the polyphonic pitch estimation has been proved to be a very difficult task. Here, the main purpose is to calculate pitch for classification of The Radif of Mirzâ Abdollâh which has been played by two famous stringed instruments the Tar and the Setar

A Threshold Adaptation based Voice Query Transcription Scheme for Music Retrieval

This paper presents a threshold adaptation based voice query transcription scheme for music information retrieval. The proposed scheme analyzes monophonic voice signal and generates its transcription for diverse music retrieval applications. For accurate transcription, we propose several advanced features including (i) Energetic Feature eXtractor (EFX) for onset, peak, and transient area detection; (ii) Modified Windowed Average Energy (MWAE) for defining multiple small but coherent windows with local threshold values as offset detector; and finally (iii) Circular Average Magnitude Difference Function (CAMDF) for accurate acquisition of fundamental frequency (F0) of each frame. In order to evaluate the performance of our proposed scheme, we implemented a prototype music transcription system called AMT2 (Automatic Music Transcriber version 2) and carried out various experiments. In the experiment, we used QBSH corpus [1], adapted in MIREX 2006 contest data set. Experimental result shows that our proposed scheme can improve the transcription performance.