Audio Classification via Mobile-Server Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies lack an efficient method to classify audio data as music or non-music in real-time, especially on devices with limited resources, making it difficult to identify and provide information about background music.
Innovation Solution
A system that uses a mobile computing device to detect audio data, process it into waveform data, and transmit it to a server-based environment for analysis, employing spectrogram analysis, beat detection, spectral fluctuation scoring, and peak repetition scoring to determine the likelihood of music presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio data is processed locally on mobile device, then classification accuracy is improved, but device energy consumption increases
Solution Approach 1:
The system segments the audio classification process into two parts: preliminary processing and analysis on the mobile device, and comprehensive spectrogram analysis on the server. This division allows the mobile device to perform only lightweight operations (energy-efficient), while the energy-intensive spectrogram computation is offloaded to the server, thus maintaining classification accuracy without excessive energy consumption on the device.
Solution Approach 2:
The system introduces an intermediary server that receives audio data from the mobile device and performs the computationally intensive spectrogram analysis. This intermediary architecture enables accurate music classification by leveraging server resources, while the mobile device conserves energy by avoiding heavy local computation.
2Measurement precision
If comprehensive spectrogram analysis is performed, then music detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The computational workload is segmented between mobile device and server. The mobile device performs simple audio capture and transmission, while the server executes the complex spectrogram analysis involving beat detection, pitch detection, and spectral fluctuation analysis. This segmentation achieves high detection accuracy through comprehensive analysis while keeping device complexity low.
Solution Approach 2:
The system replaces the need for complex local mechanical/computational processing on the mobile device with server-based processing. The mobile device simply transmits raw audio data, substituting heavy local computation with remote analysis, thus reducing device complexity while maintaining comprehensive spectrogram analysis capabilities.
3Ease of operation
If real-time music classification is implemented, then user experience is improved, but processing speed requirements increase
Solution Approach 1:
The system implements periodic action by analyzing audio data in sequential segments rather than attempting to process entire audio streams simultaneously. The mobile device captures and transmits audio in manageable chunks, and the server processes these segments sequentially, generating classification results in real-time as each segment is analyzed. This periodic processing approach maintains responsive user experience while managing processing speed requirements.
Solution Approach 2:
The system performs preliminary action by having the mobile device capture and prepare audio data for transmission before server analysis begins. This preliminary local processing (audio capture and encoding) ensures that when the server receives the data, it can immediately begin spectrogram analysis, reducing overall latency and maintaining real-time responsiveness for the user.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for analyzing an audio sample to determine whether the audio sample includes music audio data. One or more detectors, including a spectral fluctuation detector, a peak repetition detector, and a beat pitch detector, may analyze the audio sample and generate a score that represents whether the audio sample includes music audio data. One or more of the scores may be combined to determine whether the audio sample includes music audio data or non-music audio data.