Voice Noise Reduction¶

Voice Noise Reduction in Professional Audio Equipment¶

In professional audio equipment, voice noise reduction is a crucial component. With the rapid development of AI, various AI noise reduction methods have emerged. These technologies are widely applied in video conferencing, voice and video calls, voice recognition, multimedia content creation, public address systems, and more. Through continuous algorithm improvements and hardware optimization, these technologies can significantly enhance audio quality in various communication scenarios, thereby improving the user experience.

Voice Noise Reduction in Professional Audio Equipment¶

In professional audio equipment, voice noise reduction is a crucial component. With the rapid development of AI, various AI noise reduction methods have emerged. These technologies are widely applied in video conferencing, voice and video calls, voice recognition, multimedia content creation, public address systems, and more. Through continuous algorithm improvements and hardware optimization, these technologies can significantly enhance audio quality in various communication scenarios, thereby improving the user experience.

1. AI Noise Reduction¶

AI noise reduction technology uses complex algorithms and machine learning to identify and suppress background noise in audio and video signals. The main steps include:

Noise Identification: AI algorithms analyze the input signal to distinguish between desired sounds (such as speech, music) and background noise. Noise Suppression: Advanced filtering techniques are applied to reduce or eliminate noise while preserving the integrity of the desired signal. Signal Enhancement: After noise suppression, signal equalization, dynamic range compression, and speech enhancement algorithms are used to improve audio quality. Real-Time Processing: Optimizing algorithms and hardware architecture to achieve real-time noise suppression suitable for live broadcasts and real-time communication scenarios.

2. Deep Neural Networks (DNN)¶

Deep neural networks mimic the structure and function of the human brain, capable of extracting relevant features from audio signals and accurately predicting noise. These networks can capture complex relationships in audio data and precisely adjust to suppress noise while maintaining the clarity of desired audio.

3. Spectral Subtraction¶

Spectral subtraction involves estimating the noise spectrum and subtracting these spectra from the observed noisy signal to obtain a clearer version of the audio. This technique uses mathematical operations to model the spectral characteristics of the noise and perform subtraction.

4. Adaptive Filtering¶

Adaptive filtering technology dynamically adjusts noise suppression parameters in real-time while analyzing the input audio signal, adapting to changing noise environments. This technology can effectively track and reduce noise even in dynamic and unpredictable environments.

5. Broadcast Applications¶

Broadcast applications use AI to eliminate background noise and echo. By pressing a button, background noises such as keyboard clicks, microphone static, and fan noise can be quickly removed, making voice communication in live broadcasts and remote meetings clearer.

6. Targeted Voice Hearing Systems¶

This system uses AI and neural networks to identify and filter specific sounds, allowing only certain voices to pass through in noisy environments. By training a "student" model with a "teacher" model, even small models can run efficiently on devices with limited computing power and battery life.

As a professional audio solutions expert, the TurnKey solution for voice noise reduction involves hardware and software integration for rapid deployment and efficient noise reduction effects. Below is a detailed explanation of specific practices and principles:

Specific Practices¶

Hardware Selection and Configuration:¶

Microphones: Choose high-quality microphones, some of which have built-in noise suppression features. For example, the 7-microphone array technology: a 6+1 MIC array uses Beamforming technology to precisely control the pickup direction, with a side lobe suppression of up to 40dB, significantly reducing external noise interference. DSP (Digital Signal Processor): Select DSP chips with strong processing capabilities to ensure real-time audio signal processing. Other Audio Hardware: Including amplifiers and filters to optimize signal transmission and processing. Software Configuration and Algorithm Development:

Preprocessing Module: Preprocess the input audio signal, such as automatic gain control (AGC) and high-pass filters, to remove low-frequency noise and enhance signal quality. Noise Reduction Algorithm: Use advanced noise reduction algorithms, such as adaptive filters (e.g., LMS, NLMS algorithms), frequency-domain noise reduction (e.g., spectral subtraction), and deep learning algorithms (e.g., neural network-based noise reduction). Post-Processing Module: Post-process the audio signal after noise suppression, such as echo cancellation (AEC) and dynamic range compression, to further enhance audio quality. System Integration and Optimization:

Embedded System Development: Integrate hardware and software into an embedded system for firmware development and system debugging. Real-Time Performance Optimization: Optimize algorithms and hardware acceleration to ensure the system can process high-quality audio signals in real-time. User Interface Design: Develop user-friendly interfaces to allow users to set and adjust noise reduction parameters easily. Principles

Adaptive Filters:¶

Principle: Adaptive filters adjust their parameters to minimize the error signal, thereby suppressing noise. Common adaptive algorithms include LMS (Least Mean Squares) and NLMS (Normalized Least Mean Squares).
Advantages: Can adapt to changing noise environments in real-time, providing good results.

Frequency-Domain Noise Reduction:¶

Principle: Convert the audio signal to the frequency domain (e.g., via Fourier Transform), suppress the noise in the frequency domain, and then convert it back to the time domain. Common methods include spectral subtraction and Wiener filtering.
Advantages: Can target specific frequency ranges of noise, resulting in significant improvements.

Deep Learning Noise Reduction:¶

Principle: Train neural network models to learn how to separate noise from speech signals. Common models include convolutional neural networks (CNN) and recurrent neural networks (RNN).
Advantages: Strong adaptability to complex noise environments, providing excellent noise reduction performance.

Examples and Applications¶

Teleconferencing Systems: Integrating noise reduction solutions in teleconferencing systems can significantly improve call quality by reducing background noise interference.
Voice Assistants and Smart Devices: Applying noise reduction technology in smart devices can improve the accuracy of voice recognition and enhance user experience.
Hearing Assistive Devices: In devices like hearing aids, noise reduction technology can help users hear sounds more clearly.
Phaten Andio's FTXU316_LA_7MIC_V1 is a no-touch local amplification PCBA kit designed based on the above practices and principles. It is an efficient voice noise reduction TurnKey solution aimed at providing speakers with an unconstrained amplification experience while ensuring listeners enjoy clear, low-latency auditory pleasure.

Phaten Audio Solution Contact:
Allen Su
Email: hua@phaten.com