Schlüter et al., 2018 - Google Patents
Zero-Mean Convolutions for Level-Invariant Singing Voice Detection.Schlüter et al., 2018
View PDF- Document ID
- 3740383475648881056
- Author
- Schlüter J
- Lehner B
- Publication year
- Publication venue
- ISMIR
External Links
Snippet
State-of-the-art singing voice detectors are based on classifiers trained on annotated examples. As recently shown, such detectors have an important weakness: Since singing voice is correlated with sound level in training data, classifiers learn to become sensitive to …
- 238000001514 detection method 0 title description 10
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
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6228—Selecting the most significant subset of features
-
- 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
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- 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
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
-
- 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
- G10L25/90—Pitch determination of speech signals
-
- 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
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/04—Segmentation; Word boundary detection
-
- 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
-
- 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
-
- 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
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10H—ELECTROPHONIC MUSICAL INSTRUMENTS
- G10H1/00—Details of electrophonic musical instruments
- G10H1/0008—Associated control or indicating means
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Schlüter et al. | Zero-Mean Convolutions for Level-Invariant Singing Voice Detection. | |
Murthy et al. | Content-based music information retrieval (cb-mir) and its applications toward the music industry: A review | |
Pons et al. | Experimenting with musically motivated convolutional neural networks | |
Dixon et al. | Towards Characterisation of Music via Rhythmic Patterns. | |
Lehner et al. | A low-latency, real-time-capable singing voice detection method with LSTM recurrent neural networks | |
US10089578B2 (en) | Automatic prediction of acoustic attributes from an audio signal | |
US8437869B1 (en) | Deconstructing electronic media stream into human recognizable portions | |
Lehner et al. | Towards Light-Weight, Real-Time-Capable Singing Voice Detection. | |
Gulati et al. | Mining melodic patterns in large audio collections of Indian art music | |
Mounika et al. | Music genre classification using deep learning | |
Joshi et al. | Indian Classical Raga Identification using Machine Learning. | |
Dhall et al. | Music genre classification with convolutional neural networks and comparison with f, q, and mel spectrogram-based images | |
Ahrendt et al. | Decision time horizon for music genre classification using short time features | |
Prashanthi et al. | Music genre categorization using machine learning algorithms | |
US20180173400A1 (en) | Media Content Selection | |
Ullrich et al. | Music transcription with convolutional sequence-to-sequence models | |
Balachandra et al. | Music genre classification for indian music genres | |
Lukashevich et al. | From Multi-Labeling to Multi-Domain-Labeling: A Novel Two-Dimensional Approach to Music Genre Classification. | |
Muthumari et al. | A novel model for emotion detection with multilayer perceptron neural network | |
Jahnavi et al. | A Comparative Performance Evaluation of Machine Learning Approaches for Spectrogram-based Music Genre Classification | |
KR20200118587A (en) | Music recommendation system using intrinsic information of music | |
Fuhrmann et al. | Quantifying the Relevance of Locally Extracted Information for Musical Instrument Recognition from Entire Pieces of Music. | |
Kum et al. | Classification-based singing melody extraction using Deep Convolutional Neural Networks | |
Koerich et al. | Combination of homogeneous classifiers for musical genre classification | |
T. M et al. | Music genre classification using convolution temporal pooling network |