Speaker Enclosure Wall Detection Using Neural Network Audio Analysis
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Solution Overview
Problem
Portable acoustic speakers face degradation in user experience due to sound reflections from nearby walls, leading to delayed audio signals and interference.
Innovation Solution
An enclosure with a loudspeaker, multiple microphones, and an electronic wall detection device that uses a neural network model (X) to calculate spectrograms and energy levels of incoming audio signals to determine the presence and position of walls, and adapt the audio emission accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If the speaker is positioned close to a wall, then the sound intensity reaches the user, but the reflected sound causes delays and interference degrading the user experience
Solution Approach 1:
The system uses the reflected sound waves, which were previously harmful, as useful information to detect wall presence and position. By analyzing these reflections through microphones and neural networks, the system identifies environmental features and adapts audio emission accordingly, converting the harmful reflection into a beneficial detection mechanism.
Solution Approach 2:
The system dynamically changes audio emission parameters based on detected wall position and environment. The neural network model adjusts emission characteristics such as direction, intensity, and timing to compensate for reflections, transforming the static audio emission into an adaptive process that responds to environmental conditions.
2Adaptability or versatility
If the speaker emits audio signals in all directions, then the sound reaches users from different positions, but reflections from walls cause delayed signals and interference
Solution Approach 1:
The audio emission system transitions from a static omnidirectional mode to a dynamic adaptive mode. The neural network continuously processes environmental information and adjusts emission parameters in real-time, enabling the system to maintain audio coverage while adapting to wall positions and reflection patterns.
Solution Approach 2:
The system implements feedback by using microphones to capture reflected sounds and feeding this information back to the neural network. This closed-loop approach allows the system to learn from environmental reflections and adjust emission strategies to minimize interference while maintaining coverage.
3Measurement precision
If the system uses multiple microphones and neural networks to detect walls, then wall position detection accuracy improves, but the device complexity increases
Solution Approach 1:
The microphone array serves multiple functions: it captures audio signals for normal playback, detects reflected sounds for wall position detection, and provides spatial information for audio rendering. This multi-functionality reduces the need for separate dedicated detection hardware, managing complexity while maintaining detection accuracy.
Solution Approach 2:
The system uses its own emitted audio signals as the detection source, eliminating the need for separate active sensing emissions. The reflected playback signals serve dual purposes: delivering audio content to users and providing detection data for wall position estimation, enabling the system to self-service its detection needs.
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
The system effectively detects the presence and position of walls, allowing for adaptive audio emission that minimizes sound reflections and enhances user experience by reducing audio delays and interference.
Implementation Method 1
when the speaker is positioned close to a wall, for example less than 50 cm from it, the emitted sounds reflect off the wall and propagate to a user with a delay compared to sounds following a direct path between the speaker and the user
Data Source
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AI summary
The present invention relates to an enclosure (10) comprising: - at least one loudspeaker (20) for emitting an audio signal, - several microphones (25) for acquiring a received audio signal, if a wall (15) is present in the enclosure's environment, the received audio signal including the audio signal reflected from the wall, and - an electronic wall detection device (30) comprising a first processing module (35) for calculating a spectrogram of the arrival directions of the received audio signal. The detection device is characterized in that it further comprises: o a second processing module (40) for calculating an energy level of the audio signal received from each microphone, and o a determination module (45) for determining the presence or absence of the wall in the enclosure's environment using a neural network model.