[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Li et al., 2020 - Google Patents

Fusion of Hilbert-Huang transform and deep convolutional neural network for predominant musical instruments recognition

Li et al., 2020

Document ID
13176774823867825355
Author
Li X
Wang K
Soraghan J
Ren J
Publication year
Publication venue
Artificial Intelligence in Music, Sound, Art and Design: 9th International Conference, EvoMUSART 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings 9

External Links

Snippet

As a subset of music information retrieval (MIR), predominant musical instruments recognition (PMIR) has attracted substantial interest in recent years due to its uniqueness and high commercial value in key areas of music analysis such as music retrieval and …
Continue reading at link.springer.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3074—Audio data retrieval
    • G06F17/30743—Audio data retrieval using features automatically derived from the audio content, e.g. descriptors, fingerprints, signatures, MEP-cepstral coefficients, musical score, tempo
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3074—Audio data retrieval
    • G06F17/30755—Query formulation specially adapted for audio data retrieval
    • G06F17/30758—Query by example, e.g. query by humming
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H2210/00—Aspects or methods of musical processing having intrinsic musical character, i.e. involving musical theory or musical parameters or relying on musical knowledge, as applied in electrophonic musical tools or instruments
    • G10H2210/031—Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal
    • G10H2210/061—Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal for extraction of musical phrases, isolation of musically relevant segments, e.g. musical thumbnail generation, or for temporal structure analysis of a musical piece, e.g. determination of the movement sequence of a musical work
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H2210/00—Aspects or methods of musical processing having intrinsic musical character, i.e. involving musical theory or musical parameters or relying on musical knowledge, as applied in electrophonic musical tools or instruments
    • G10H2210/031—Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal
    • G10H2210/066—Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal for pitch analysis as part of wider processing for musical purposes, e.g. transcription, musical performance evaluation; Pitch recognition, e.g. in polyphonic sounds; Estimation or use of missing fundamental
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3074—Audio data retrieval
    • G06F17/30749—Audio data retrieval using information manually generated or using information not derived from the audio data, e.g. title and artist information, time and location information, usage information, user ratings
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification
    • G10L17/26—Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H1/00—Details of electrophonic musical instruments
    • G10H1/0008—Associated control or indicating means
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H3/00—Instruments in which the tones are generated by electromechanical means
    • G10H3/12—Instruments in which the tones are generated by electromechanical means using mechanical resonant generators, e.g. strings or percussive instruments, the tones of which are picked up by electromechanical transducers, the electrical signals being further manipulated or amplified and subsequently converted to sound by a loudspeaker or equivalent instrument
    • G10H3/125—Extracting or recognising the pitch or fundamental frequency of the picked up signal
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H2250/00—Aspects of algorithms or signal processing methods without intrinsic musical character, yet specifically adapted for or used in electrophonic musical processing
    • G10H2250/131—Mathematical functions for musical analysis, processing, synthesis or composition
    • G10H2250/215—Transforms, i.e. mathematical transforms into domains appropriate for musical signal processing, coding or compression
    • G10H2250/235—Fourier transform; Discrete Fourier Transform [DFT]; Fast Fourier Transform [FFT]
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H3/00—Instruments in which the tones are generated by electromechanical means
    • G10H3/12—Instruments in which the tones are generated by electromechanical means using mechanical resonant generators, e.g. strings or percussive instruments, the tones of which are picked up by electromechanical transducers, the electrical signals being further manipulated or amplified and subsequently converted to sound by a loudspeaker or equivalent instrument
    • G10H3/14—Instruments in which the tones are generated by electromechanical means using mechanical resonant generators, e.g. strings or percussive instruments, the tones of which are picked up by electromechanical transducers, the electrical signals being further manipulated or amplified and subsequently converted to sound by a loudspeaker or equivalent instrument using mechanically actuated vibrators with pick-up means
    • G10H3/18—Instruments in which the tones are generated by electromechanical means using mechanical resonant generators, e.g. strings or percussive instruments, the tones of which are picked up by electromechanical transducers, the electrical signals being further manipulated or amplified and subsequently converted to sound by a loudspeaker or equivalent instrument using mechanically actuated vibrators with pick-up means using a string, e.g. electric guitar
    • G10H3/181—Details of pick-up assemblies
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
    • G10H2240/00—Data organisation or data communication aspects, specifically adapted for electrophonic musical tools or instruments
    • G10H2240/121—Musical libraries, i.e. musical databases indexed by musical parameters, wavetables, indexing schemes using musical parameters, musical rule bases or knowledge bases, e.g. for automatic composing methods
    • G10H2240/131—Library retrieval, i.e. searching a database or selecting a specific musical piece, segment, pattern, rule or parameter set

Similar Documents

Publication Publication Date Title
Ghosal et al. Music Genre Recognition Using Deep Neural Networks and Transfer Learning.
Bhalke et al. Automatic musical instrument classification using fractional fourier transform based-MFCC features and counter propagation neural network
Caetano et al. Audio content descriptors of timbre
Cai et al. Music genre classification based on auditory image, spectral and acoustic features
Li et al. Fusion of Hilbert-Huang transform and deep convolutional neural network for predominant musical instruments recognition
Lostanlen et al. Time–frequency scattering accurately models auditory similarities between instrumental playing techniques
Monir et al. Singing voice detection: a survey
Chakraborty et al. Improved musical instrument classification using cepstral coefficients and neural networks
Nkambule et al. Classification of music by genre using probabilistic models and deep learning models
Srinivasa Murthy et al. Singer identification for Indian singers using convolutional neural networks
Mistry et al. Time-frequency visual representation and texture features for audio applications: a comprehensive review, recent trends, and challenges
Dutta et al. CNN based musical instrument identification using time‐frequency localized features
Dhall et al. Music genre classification with convolutional neural networks and comparison with f, q, and mel spectrogram-based images
Lerch Audio content analysis
Alar et al. Audio classification of violin bowing techniques: An aid for beginners
Ghosh et al. Music instrument identification based on a 2-d representation
Kumar et al. Melody extraction from music: A comprehensive study
Cheng Music information retrieval technology: Fusion of music, artificial intelligence and blockchain
Rajan et al. Multi-channel CNN-Based Rāga Recognition in Carnatic Music Using Sequential Aggregation Strategy
Joshi et al. Identification of Indian musical instruments by feature analysis with different classifiers
Bormane et al. A novel techniques for classification of musical instruments
Lai [Retracted] Automatic Music Classification Model Based on Instantaneous Frequency and CNNs in High Noise Environment
Ghosal et al. Stratification of String Instruments Using Chroma-Based Features
Dash et al. A Comprehensive Review on Audio based Musical Instrument Recognition: Human-Machine Interaction towards Industry 4.0
Kumari et al. CLASSIFICATION OF NORTH INDIAN MUSICAL INSTRUMENTS USING SPECTRAL FEATURES.