Singing Voice Liveness Detection With Fakeprint Embeddings
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Solution Overview
Problem
Conventional biometric and anti-spoofing systems fail to accurately detect machine-generated singing vocals due to significant differences in acoustic features between singing and speech signals, leading to reduced accuracy and robustness, and lack mechanisms for isolating singing vocals from instrumental audio and identifying specific human singers.
Innovation Solution
A multi-stage machine-learning architecture comprising a singing detector, singer detector, and liveness detector, with data augmentation techniques like pitch shifting and compression artifact simulation, to identify and classify machine-generated singing vocals, and singer-specific vocalprints for attribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech-based detection systems are used, then the system complexity remains low, but the detection accuracy for singing vocals deteriorates due to significant acoustic feature differences
Solution Approach 1:
The system segments the detection task into multiple specialized components: a singing detector that identifies vocal segments, a singer detector that extracts vocal identity features, and a liveness detector that generates authenticity scores. This segmentation allows each component to be optimized for its specific function, improving overall detection accuracy while managing complexity through modular design.
Solution Approach 2:
The machine learning architecture is designed with multi-functionality to handle both speech and singing vocal detection. The system uses universal acoustic feature extraction that adapts to different vocal types, and the liveness detector can process both speech-based and singing-based inputs, making the system versatile without requiring completely separate detection pipelines.
2Adaptability or versatility
If speech-optimized detection systems are used, then the model training is simpler, but the generalization capability to singing vocals deteriorates
Solution Approach 1:
The system applies singing-specific data augmentation techniques that transform training data parameters: pitch shifting to simulate different vocal ranges, tempo perturbation to handle varying speeds, tremolo modulation to add amplitude variations, loudness normalization to standardize volume, and compression artifact simulation to mimic real-world audio degradation. These parameter changes enable the model to generalize better to singing vocals while managing training complexity through automated augmentation pipelines.
3Measurement precision
If conventional biometric systems are used, then the processing speed remains high, but the ability to identify specific singers deteriorates due to lack of singer-specific feature extraction
Solution Approach 1:
The system extracts and isolates singer-specific features by separating the vocal signal processing from the instrumental accompaniment. The singer detector specifically extracts vocalprint embeddings that represent unique singer characteristics, taking out only the relevant vocal identity information from the mixed audio signal. This extraction approach enables accurate singer identification while maintaining efficient processing by focusing computational resources on the most discriminative features.
Data Source
AI summary
Disclosed are systems and methods including software processes executed by a server that detect machine-generated synthetic singing vocals in a vocal audio signal of an audio signal using a multi-stage machine-learning architecture. A singing detector identifies vocal segments containing singing. A singing liveness detector includes a fakeprint embedding extractor that extracts fakeprint feature vector embeddings representing artifacts of machine-generated vocal signals, scoring layers or classifier layers to generate a singing liveness score for identifying the likelihood a vocal signal is human-generated or synthetic. An optional singer detector includes a vocalprint embedding extractor that extracts vocalprint feature vector embeddings representing singer-specific vocal identity characteristics and generates a singer identification score or attribution score for identifying a particular singer in the vocal signal.