Local Call Recording System Using Offline ML for VoIP
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Solution Overview
Problem
Existing call recording systems for VoIP networks face challenges such as high integration costs, reduced call quality due to quality denominator issues, bandwidth overload, and limitations in recording across platforms, especially when processing power is limited, requiring integration with third-party software or external servers.
Innovation Solution
A call recording system that uses a combination of rules-based and machine learning models to analyze audio representations, determining the presence of active calls without relying on external software, and storing them locally to avoid bandwidth overload, utilizing a sound receiver, buffer, call candidate determination unit, and storage system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If speech analysis methods with keywords are used to detect calls, then call detection capability is improved, but processing power requirements increase significantly
Solution Approach 1:
The speech analysis process is segmented into two distinct stages: an offline training phase where the system learns to recognize call patterns from labeled audio data, and an online detection phase where the trained model efficiently classifies incoming audio. This segmentation allows complex processing to be done once during training, while runtime detection requires minimal processing power.
Solution Approach 2:
The system performs preliminary action by training machine learning models offline before actual call detection begins. During the offline training phase, the system processes large amounts of speech data to build acoustic models and feature extractors. When real calls come in, the pre-trained model can quickly classify them without requiring significant processing power at runtime.
2Ease of operation
If call recording is integrated with VoIP provider, then recording functionality is achieved, but integration cost and complexity increase
Solution Approach 1:
The recording functionality is extracted from the VoIP provider system and implemented as an independent local application. Instead of integrating with the provider's infrastructure, the system captures audio directly from the device's audio interface, eliminating the need for complex API integrations, authentication mechanisms, and dependency on external services.
Solution Approach 2:
The system provides self-service by implementing all recording capabilities locally on the user's device. It independently detects calls, records audio, and manages storage without requiring external servers, authentication tokens, or provider-specific integration code. The application is platform-agnostic and works with any VoIP service through the device's audio interface.
3Ease of operation
If third party API integration is used, then call recording capability is obtained, but bandwidth consumption increases
Solution Approach 1:
The system extracts the audio recording function from network-dependent solutions and implements it locally. By capturing audio directly from the device's audio interface rather than transmitting call data to external servers for processing, the system eliminates continuous network communication and associated bandwidth consumption.
Solution Approach 2:
Instead of relying on the VoIP provider's audio streams which require network transmission, the system creates a local copy of the audio through the device's audio interface. This local copying approach avoids the need to send audio data over the network for analysis or storage, significantly reducing bandwidth usage.
Data Source
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AI summary
The present invention relates to a call recording system and a call recording method for automatically recording, i.e. storing, a call candidate when an active call is detected. The call recording system comprises a sound receiver to receive sound data and to convert sound data to audio representations of sound, a buffer to buffer the audio representations of sound for a predetermined time duration, a call candidate determination unit to determine if the buffered audio representations comprise a call candidate, a call analyzer to analyze the call candidate, wherein the call analyzer determines if the call candidate is a call to be stored, and a storage to store the call candidate as a call. Hence, a reliable system can be provided for automatically storing a call.