Hierarchical LSTM-ResNet Audio Model for Media Presence Detection
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately distinguishing between human speech and media sounds, particularly due to weakly labeled training data and ambiguities in audio snippets, which affects the reliability of media presence detection and subsequent actions in voice-assisted devices.
Innovation Solution
A system utilizing a hierarchical machine learning model architecture that combines a Long Short-Term Memory (LSTM) network with a residual network to analyze audio segments, leveraging contextual information from longer audio streams to improve the detection of media presence by extracting signal-level representations and providing more precise start and end points for media events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition systems are used to detect media presence, then the system structure remains simple, but the accuracy of distinguishing human speech from media sounds deteriorates due to weakly labeled training data and audio snippet ambiguities
Solution Approach 1:
The audio data is segmented into multiple audio segments, and the hierarchical model processes these segments at different levels. The first level extracts features from individual segments, while the second level aggregates contextual information across segments, enabling precise media presence detection through structured decomposition of the complex detection task.
Solution Approach 2:
The patent implements a hierarchical machine learning model where a first level model processes individual audio segments and a second level model processes the outputs from the first level model. This nested structure allows the system to handle complexity at multiple levels, with the second level providing contextual aggregation over the first level's segment-level predictions, thereby improving detection accuracy while managing system complexity through structured organization.
2Reliability
If media presence detection is performed without contextual information from longer audio streams, then processing speed increases, but detection reliability deteriorates due to ambiguities in short audio snippets
Solution Approach 1:
The system performs preliminary feature extraction at the first level model for individual audio segments before proceeding to the second level model for contextual aggregation. This preliminary processing allows the system to prepare and filter information efficiently, reducing the computational burden on longer audio stream processing while maintaining detection reliability through structured multi-level analysis.
Solution Approach 2:
The hierarchical model adds a temporal context dimension by processing audio segments at multiple levels. The first level handles local segment characteristics, while the second level introduces temporal context from longer audio streams. This dimensional expansion from segment-level to stream-level processing enables the system to resolve ambiguities in short snippets using contextual information without requiring processing of the entire long stream at once.
3Productivity
If the system processes entire long audio streams at once, then contextual information is captured comprehensively, but computational efficiency deteriorates
Solution Approach 1:
The audio stream is divided into multiple segments that are processed independently at the first level model, then aggregated at the second level. This segmentation enables parallel processing and reduces computational complexity compared to processing the entire long stream at once, while the hierarchical aggregation preserves contextual information by combining segment-level features with temporal context from longer streams.
Solution Approach 2:
The system processes only the necessary portions of audio data at each level. The first level model processes individual segments selectively, and the second level model processes only the outputs from the first level that require contextual aggregation. This partial processing approach maintains computational efficiency by avoiding unnecessary processing of entire long audio streams while still capturing essential contextual information where needed.
Data Source
AI summary
Described herein is a system for media presence detection in audio. The system analyzes audio data to recognize whether a given audio segment contains sounds from a media source as a way of differentiating recorded media source sounds from other live sounds. In exemplary embodiments, the system includes a hierarchical model architecture for processing audio data segments, where individual audio data segments are processed by a trained machine learning model operating locally, and another trained machine learning model provides historical and contextual information to determine a score indicating the likelihood that the audio data segment contains sounds from a media source.