Location-Aware ML Sound Classification for Selective Noise Cancellation
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Solution Overview
Problem
Multimedia devices struggle to effectively distinguish between desirable and undesirable sounds in noisy environments, leading to user distraction and missed important audio cues.
Innovation Solution
A machine learning (ML) algorithm uses a global navigation satellite system to detect the user's location and identify relevant sounds, controlling an active noise cancellation system to cancel undesirable sounds based on user training, generating anti-noise waveforms to reduce noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If active noise cancellation is applied to all sounds in noisy environments, then noise reduction is improved, but desirable sounds are also cancelled causing user distraction
Solution Approach 1:
The system applies different noise cancellation treatments to different sound sources based on their classification. Desirable sounds (speech, notifications, alerts) are preserved while undesirable sounds (ambient noise, background chatter) are cancelled, creating localized quality differentiation in the audio processing pipeline
Solution Approach 2:
The audio spectrum is segmented into multiple categories using machine learning classification. Sounds are divided into desirable and undesirable segments, allowing independent processing of each segment with appropriate noise cancellation applied only to the undesirable segments
2Measurement precision
If machine learning algorithm classifies sounds based on location, then sound classification accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary location determination using GPS before sound classification. By pre-establishing the user's location context, the ML algorithm can more accurately classify sounds based on location-specific patterns, improving classification accuracy without requiring the ML model to process all raw sensor data from scratch
Solution Approach 2:
Location data acts as an intermediary feature that bridges environmental context and sound classification. The GPS location information serves as a mediator that helps the ML algorithm understand the acoustic environment without directly analyzing complex acoustic patterns, reducing computational complexity
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
Enhances user experience by allowing users to focus on relevant sounds while continuing multimedia playback, preventing missed communications or notifications in noisy environments.
Implementation Method 1
ANC uses one or more microphones to pick up an external noise reference signal, generates an anti-noise waveform from the noise reference signal, and reproduces the anti-noise waveform through one or more loudspeakers. This anti-noise waveform interferes destructively with the original noise wave to reduce the level of the noise that reaches the ear of the user.
Data Source
AI summary
This disclosure provides systems, methods, and devices for audio signal processing that support noise cancellation. In a first aspect, a method of signal processing includes determining a location of the apparatus; receiving an audio signal including sounds at the location of the apparatus; determining, based on a machine learning (ML) model, to reduce a presence of the one or more sounds in the audio signal based on the location; and determining an output audio signal by reducing the presence of the one or more sounds in the audio signal. Other aspects and features are also claimed and described.