Machine-Learned Audio Filter Selection for Devices and Room Acoustics
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Solution Overview
Problem
Existing audio playback systems lack an effective method to select audio filters that account for the audio output device, listening environment, and audio track features, leading to subjective and non-scalable audio perception issues.
Innovation Solution
A system and method for generating feature-to-filter mapping functions based on audio track, output device, and physical space acoustics to optimize audio filter selection, using machine learning and clustering to map audio filters to these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subjective listening tests are used to select audio filters, then human perception can be considered, but the process is non-scalable and lacks reliability
Solution Approach 1:
The patent replaces subjective human listening tests with an automated machine learning system that uses audio feature extraction and clustering algorithms. The system automatically analyzes audio tracks, extracts features (spectral centroid, spectral flux, rhythm, etc.), clusters them into categories, and maps filters to clusters without human intervention, thereby eliminating scalability limitations and improving reliability through consistent algorithmic decision-making
Solution Approach 2:
The system performs self-service by automatically analyzing audio tracks and selecting appropriate filters without requiring human testers. The machine learning model independently processes audio features, performs clustering, and determines optimal filter mappings, enabling the system to serve itself rather than relying on subjective human evaluation
2Adaptability or versatility
If a limited number of preconfigured audio filters are provided, then device complexity is reduced, but adaptability to different audio tracks and environments is insufficient
Solution Approach 1:
The patent implements dynamic filter selection where the system automatically determines which filters to apply based on real-time analysis of audio track features and listening environment characteristics. Rather than providing a static limited set of preconfigured filters, the system dynamically adapts the filter selection to match the specific audio content and environment, effectively increasing versatility without requiring users to manually configure complex settings
Solution Approach 2:
The system performs preliminary analysis of audio track features and environment characteristics before filter application. By pre-extracting features (spectral centroid, spectral flux, rhythm, envelope, zero-crossing rate) and clustering them into categories, the system prepares the groundwork for optimal filter selection in advance, enabling adaptive filter mapping without increasing user-facing complexity
3Reliability
If audio filters are applied to compensate for device limitations and environment, then audio quality improves, but tonal changes may become annoying and worse than original audio
Solution Approach 1:
The patent applies local quality by selecting and applying different audio filters tailored to specific local characteristics of each audio track and environment. Rather than applying a uniform filter set, the system analyzes local features (spectral centroid, spectral flux, rhythm, envelope, zero-crossing rate) and selects filters that specifically address the unique characteristics of each audio segment, minimizing unwanted tonal changes while maximizing compensation effectiveness
Solution Approach 2:
The system changes parameters by dynamically adjusting filter characteristics based on extracted audio features. The machine learning model maps audio track parameters (spectral centroid, spectral flux, rhythm, envelope, zero-crossing rate) to appropriate filter settings, continuously adapting filter parameters to match the audio content and environment, thereby achieving quality compensation without introducing harmful tonal alterations
Data Source
AI summary
A training audio track feature vector is generated for training audio tracks. The training audio track feature vector includes training track vector components based on one or more feature sets. Each of the training track vector components is grouped into at least one cluster. Audio filters are mapped to one or more of the clusters, thereby building a feature-filter mapping function. Mapping functions from filters to audio output devices and/or physical space acoustic features can also be built. A media playback device receives the mapping function(s) and is enabled to apply the mapping function(s) to a query audio track feature vector to identify at least one audio filter corresponding to the query audio track. The media playback device can then apply the at least one audio filter to the query audio track.