Audio Fingerprinting for Live Performance Variations
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Solution Overview
Problem
Existing systems fail to accurately identify audio pieces during live performances, especially when variations such as tempo, vocal timber, and instrument tuning differ from recorded versions, and lack efficient methods for real-time identification and metadata provision to audience members.
Innovation Solution
A network-based system using audio fingerprinting techniques, particularly a chromagram-based approach, to identify live audio performances by comparing live fingerprints to a set of reference fingerprints, even in the presence of variations, and provide real-time identification and metadata to audience members through devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audio recognition systems are used to identify music during live performances, then the system can process audio data, but it fails to accurately identify audio pieces when variations in tempo, vocal timber, and instrument tuning occur
Solution Approach 1:
The system transforms the audio signal into a chromagram representation, which normalizes pitch and tempo variations. This parameter transformation allows the system to recognize music patterns regardless of live performance variations in tempo, key, or instrument tuning, thereby improving both accuracy and adaptability simultaneously
Solution Approach 2:
The system creates a fingerprint copy of the audio piece's spectral characteristics through chromagram generation. This fingerprint serves as a normalized representation that can be matched against reference fingerprints without being affected by performance variations, enabling accurate identification while adapting to live performance conditions
2Reliability
If real-time audio fingerprinting is implemented for live performance identification, then accurate identification can be achieved, but the system complexity increases
Solution Approach 1:
The audio analysis process is segmented into distinct functional stages: audio signal acquisition, chromagram generation, fingerprint creation, and pattern matching. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining real-time performance capability
Solution Approach 2:
The chromagram serves as an intermediary representation between the raw audio signal and the final fingerprint matching process. This intermediate form simplifies the data structure and enables efficient comparison without requiring complex real-time analysis, thereby reducing system complexity while maintaining reliability
3Productivity
If audio identification is performed during live performances, then audience members can access metadata in real-time, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes reference audio recordings to generate and store their chromagram fingerprints before the live performance. During the performance, only the incoming audio needs to be transformed and compared against the pre-computed references, significantly reducing real-time computational requirements and enabling fast metadata provision
Solution Approach 2:
By transforming audio into a normalized chromagram parameter space, the system reduces the computational complexity of pattern matching. This parameter transformation enables efficient real-time identification with lower computational resource consumption compared to direct time-domain analysis
Data Source
AI summary
Methods and apparatus for audio identification during a performance are disclosed herein. An example apparatus includes at least one memory and at least one processor to transform a segment of audio into a log-frequency spectrogram based on a constant Q transform using a logarithmic frequency resolution, transform the log-frequency spectrogram into a binary image, each pixel of the binary image corresponding to a time frame and frequency channel pair, each frequency channel representing a corresponding quarter tone frequency channel in a range from C3-C8, generate a matrix product of the binary image and a plurality of reference fingerprints, normalize the matrix product to form a similarity matrix, select an alignment of a line in the similarity matrix that intersects one or more bins in the similarity matrix with the largest calculated Hamming similarities, and select a reference fingerprint based on the alignment.