Audio Recognition via FFT Fingerprint Segmentation
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Solution Overview
Problem
Current sound recognition systems face limitations due to ambient noise and processing speed issues, which affect their performance and efficiency in delivering value and convenience to consumers.
Innovation Solution
A system comprising user equipment, an audio recognition platform, and a database that generates an audio fingerprint by sampling audio signals, performing Fast Fourier Transform, and segmenting them into frequency bands to recognize audio clips, even in the presence of background interference, thereby reducing processing burden and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sound recognition methods are used, then recognition capability is achieved, but processing speed is slow and ambient noise interferes with performance
Solution Approach 1:
The audio signal is divided into multiple frequency bands through FFT transformation, and the recognition process is segmented into distinct stages: preprocessing, feature extraction, and matching. This segmentation allows parallel processing of different frequency components, improving processing speed while maintaining recognition accuracy through systematic analysis of each band independently
Solution Approach 2:
An audio fingerprint is introduced as an intermediary representation that captures the essential characteristics of the audio signal in a compressed form. This fingerprint serves as a mediator between the raw audio signal and the recognition process, enabling fast comparison and matching while being robust to ambient noise through its design that focuses on salient spectral features
2Measurement precision
If comprehensive audio analysis is performed, then recognition accuracy is improved, but processing burden on user devices and network bandwidth increase
Solution Approach 1:
The audio fingerprint is generated in advance as a pre-processed representation of the audio signal. This preliminary action extracts the essential features and stores them in a compact form, so that during recognition only a simple matching process is needed rather than performing complete audio analysis in real-time, significantly reducing the processing burden on user devices
Solution Approach 2:
The essential spectral characteristics are extracted from the full audio signal to create a condensed audio fingerprint. This extraction process removes redundant information while preserving the key features needed for recognition, allowing accurate matching with minimal processing requirements on user devices and reduced network bandwidth consumption
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively recognizes audio signals with improved processing speeds and reduced burden on user devices and network bandwidth, enabling enhanced interaction with media and games by providing flexible and efficient audio recognition.
Implementation Method 1
generating a plurality of first intensity values by performing a first FFT on the samples included in the first frame, and generating a plurality of second intensity values by performing a second FFT on the samples included in the second frame
Data Source
AI summary
A method includes generating, by a processor, an audio fingerprint representative of an audio signal. The audio fingerprint is based on a plurality of first intensity values corresponding to one or more segments of the audio signal. The plurality of first intensity values are based on a Fast Fourier Transform (FFT) performed on at least one sampled segment of the audio signal. The method also includes comparing a plurality of second intensity values based on a recorded sound to determine whether the second intensity values match the first intensity values. The method additionally includes causing a message to be communicated to a device used to record the sound based on a determination that the plurality of second intensity values match the plurality of first intensity values.