26,918 research outputs found
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Extracting Information from Music Audio
Overview of various threads of music-related research at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University
On the Mathematics of Music: From Chords to Fourier Analysis
Mathematics is a far reaching discipline and its tools appear in many
applications. In this paper we discuss its role in music and signal processing
by revisiting the use of mathematics in algorithms that can extract chord
information from recorded music. We begin with a light introduction to the
theory of music and motivate the use of Fourier analysis in audio processing.
We introduce the discrete and continuous Fourier transforms and investigate
their use in extracting important information from audio data
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Extracting and Using Music Audio Information
Surveys the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, on extracting information from music audio, and the further goals of estimating music similarity and discovering underlying structure
Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking
Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.</p
Music Similarity Estimation
Music is a complicated form of communication, where creators and culture communicate and expose their individuality. After music digitalization took place, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). To build these systems and recommend the right choice of song to the user, classification of songs is required. In this paper, we propose an approach for finding similarity between music based on mid-level attributes like pitch, midi value corresponding to pitch, interval, contour and duration and applying text based classification techniques. Our system predicts jazz, metal and ragtime for western music. The experiment to predict the genre of music is conducted based on 450 music files and maximum accuracy achieved is 95.8% across different n-grams. We have also analyzed the Indian classical Carnatic music and are classifying them based on its raga. Our system predicts Sankarabharam, Mohanam and Sindhubhairavi ragas. The experiment to predict the raga of the song is conducted based on 95 music files and the maximum accuracy achieved is 90.3% across different n-grams. Performance evaluation is done by using the accuracy score of scikit-learn
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