Machine Listening¶
Machine listening is a field of study that focuses on enabling machines to interpret and analyze audio signals. It has applications in various domains, including music, speech, and environmental sound analysis.
Information Retrieval¶
Information retrieval in machine listening involves extracting meaningful information from audio data. Key areas include:
- Classification: This involves categorizing audio into predefined classes, such as:
- Recommendation: Systems that suggest music or audio content based on user preferences. Learn more about recommender systems.
- Source Separation: The process of isolating individual sound sources from a mixture. See source separation.
- Transcription: Converting audio into symbolic representations like sheet music. Learn about music transcription.
- Question-Answering: Systems that answer questions based on audio content.
- Segmentation: Dividing audio into meaningful segments, such as verses or choruses in music.
- Feature Extraction: Extracting characteristics like pitch, tempo, or timbre from audio. See audio feature extraction.
Data¶
Machine listening relies on various types of data:
- Symbolic Data: Representations like MIDI and MusicXML.
- Subsymbolic Data: Includes raw audio, video, and sensor data.
- Metadata: Information about the audio, such as artist or album details. Learn about metadata.
- Paradata: Data about the process of data collection or analysis.
- User Data: Information about user interactions and preferences.
Artificial Intelligence (AI)¶
AI techniques in machine listening can be categorized as:
- Rule-Based Systems: Systems that rely on predefined rules and statistics.
- Learning-Based Systems: Systems that use machine learning to adapt and improve. Learn about machine learning.
Time¶
Machine listening systems can operate in different time modes:
- Realtime: Systems that process audio as it is received, such as online systems.
- Non-Realtime: Systems that process audio after it has been recorded, such as offline systems.
Citations¶
the following syntax: {cite}`holdgraf_evidence_2014`
Here is the bibliography