[go: up one dir, main page]

Pérez-Sancho et al., 2008 - Google Patents

Stochastic text models for music categorization

Pérez-Sancho et al., 2008

View PDF
Document ID
6869583285680649334
Author
Pérez-Sancho C
Rizo D
Inesta J
Publication year
Publication venue
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

External Links

Snippet

Music genre meta-data is of paramount importance for the organization of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music …
Continue reading at rua.ua.es (PDF) (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
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/3071—Clustering or classification including class or cluster creation or modification
    • 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
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634—Querying
    • G06F17/30657—Query processing
    • G06F17/30675—Query execution
    • 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
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/30707—Clustering or classification into predefined classes
    • 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
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30613—Indexing
    • G06F17/30619—Indexing indexing structures
    • 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
    • G10L15/18—Speech classification or search using natural language modelling
    • 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
    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2705—Parsing
    • G06F17/2715—Statistical methods
    • 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
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
US6185531B1 (en) Topic indexing method
Tsaptsinos Lyrics-based music genre classification using a hierarchical attention network
Pérez-Sancho et al. Genre classification using chords and stochastic language models
Bužić et al. Lyrics classification using naive bayes
US6253169B1 (en) Method for improvement accuracy of decision tree based text categorization
Whitman et al. Musical query-by-description as a multiclass learning problem
Agarwal et al. An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression model
Hedges et al. Predicting the composer and style of jazz chord progressions
Lisena et al. MIDI2vec: Learning MIDI embeddings for reliable prediction of symbolic music metadata
Carsault et al. Using musical relationships between chord labels in automatic chord extraction tasks
Salehin et al. A comparative study of different text classification approaches for bangla news classification
Kotenko et al. Evaluation of text classification techniques for inappropriate web content blocking
Ziegelmayer et al. Sentiment polarity classification using statistical data compression models
Karsdorp et al. Learning similarity metrics for melody retrieval
Lavrenko et al. Polyphonic music modeling with random fields
Ahmed et al. Machine learning and deep learning techniques for genre classification of bangla music
Sharma et al. Raga recognition using neural networks and n-grams of melodies
Sultana et al. Fake news detection using machine learning techniques
Hajj et al. Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms
Nakayama et al. Dnn-lstm-crf model for automatic audio chord recognition
Mendhakar et al. Automatic authorship classification for German lyrics using Naïve bayes
Weninger et al. Automatic assessment of singer traits in popular music: Gender, age, height and race
Gelbukh et al. Multi-instrument based N-grams for composer classification task
Vatolkin et al. Evaluation of Album Effect for Feature Selection in Music Genre Recognition.
Pérez-Sancho et al. Stochastic text models for music categorization