Acoustic Event Detection via Text-to-Audio Synthesis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing acoustic event detection systems struggle to detect custom acoustic events based on natural language descriptions, especially when no existing event profile or audio sample is available.
Innovation Solution
The system generates a profile for a new custom sound by refining the user-provided natural language description through additional questioning and aligns audio and text representation networks using multi-view model training and knowledge graphs to identify potential matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses pre-existing event profiles for acoustic event detection, then detection accuracy is improved, but the system cannot detect custom acoustic events without pre-existing profiles
Solution Approach 1:
The system performs preliminary actions by generating synthetic audio samples and creating event profiles in advance for custom acoustic events. When a user wants to detect a custom sound, the system proactively generates training data and profiles rather than requiring pre-existing profiles, enabling both custom event detection and maintaining detection accuracy through pre-prepared event profiles.
2Reliability
If the system collects and processes user audio data for model training, then detection performance is improved, but user privacy is compromised
Solution Approach 1:
The system creates copies of audio data through text-to-audio synthesis to generate training samples without collecting actual user audio recordings. The text-based representation and synthetically generated audio samples serve as substitutes for real user data, allowing model training and performance improvement while maintaining user privacy since no actual user audio needs to be collected or stored.
3Measurement precision
If the system uses multiple audio samples for training, then detection precision is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The system performs self-service by automatically generating audio samples through text-to-audio synthesis without requiring manual data collection from users. The system autonomously creates training data by synthesizing audio from text descriptions of acoustic events, eliminating the need for complex data collection infrastructure and processing pipelines while still achieving high detection precision through multiple generated samples.
Data Source
AI summary
A system configured to detect custom acoustic events, where the system generates an acoustic event profile for the custom acoustic event based on a natural language description and without a sample of the sound. The system may generate a profile for a new custom sound based on a natural language description provided by the user; for example, a “microwave beep.” If the system does not have an existing event profile for a microwave beep, the system may ask the user questions to determine whether any existing event profiles are close (e.g., is the sound similar to a “fan,”“alarm,”“appliance beep,” etc.). The system may detect an event that may be a possible match for the custom sound and ask the user to verify whether the detected event corresponds to the custom sound. The system may update the event profile based on the user's response.