A Review of the Challenges of Adaptive Filtering Technology in High-fidelity Audio Signal Processing
Zhihan Huang
Newchanel High School, Hangzhou, 310000, China
lik_tree@icloud.com
Abstract. High-fidelity audio signal processing plays an important role in modern audio technology. With the increasing demand for this technology in various audio application scenarios, the optimization of adaptive filters has emerged as a significant challenge in this field. This paper focuses on improving the performance of adaptive filters in high-fidelity audio signal processing which aims to improve the adaptability and efficiency of filters in complex audio environments by improving algorithms. In this study, the adaptive filtering algorithm based on wavelet transform and particle swarm optimization is used to verify the audio data processing by simulation and real data processing. The research data was collected using the standard audio signal database and the actual collected high-fidelity audio samples. The results show that the improved adaptive filter can significantly improve the performance of complex audio environments, improve the clarity and fidelity of audio signals and reduce the computational complexity. It is also suitable for real-time processing scenarios with limited resources. The conclusion shows that this method provides an efficient solution for high-fidelity audio signal processing and has a wide application prospect.
Keyword: Adaptive filters, High-fidelity audio, Real-time processing, Particle swarm optimization.
1 Introduction
Adaptive filters play an important role in the field of modern signal processing, especially in the processing of dynamic changes and complex environment signals, demonstrating its unique advantages. At present, adaptive filtering technology has played its unique advantages in many fields such as communication systems, image processing and speech recognition[1]. However, despite significant progress in improving signal processing accuracy and real-time performance, there are still many challenges and research gaps in dealing with non-static signal environments, reducing computational complexity, and improving filter adaptability.
The focus of this paper is to explore the application and optimization of adaptive filters in high-fidelity audio signal processing, aiming to improve the algorithm for adaptive filters to work more efficiently in different audio environments. This research will pay special attention to the following problems: Firstly, how to quickly adjust the adaptive filter in a dynamic audio environment to ensure the clarity and stability of the audio signal; Secondly, how to optimize the computational efficiency and reduce the computational complexity of the filter in the resource-limited environment. In this study, an adaptive filtering algorithm based on wavelet transform and particle swarm optimization will be used to verify the effectiveness of the proposed method through simulation and real audio data processing.
The significance of this research is to provide a more efficient and flexible solution forfuture high-fidelity audio signal processing technology. By optimizing the performance of adaptive filters, not only can the quality of audio processing be improved, but also the application of signal processing technology in other fields can be promoted. In addition, this study will also provide a new way to solve the computational complexity problem of adaptive filters in real-time processing, and put forward constructive suggestions for future technical development in related fields.
2 Characteristics and Application of Adaptive Filter
The most significant feature of the adaptive filter is that it can work effectively in uncharted environments and can track the time-varying characteristics of the input signal. The filter is estimated based on the statistical characteristics of the input and output signals. A special algorithm is used to automatically adjust the filtering coefficient to obtain the best filtering characteristics. The adaptive filter can be a continuous domain or a discrete domain, and the discrete domain adaptive filter is composed of a set of tapped delay lines, variable weighting coefficient and automatic adjustment coefficient mechanism. For each sample of the input signal sequence, the weight coefficient is updated and adjusted according to the specific algorithm. This process aims to minimize the mean square error of the output signal sequence relative to the expected output signal sequence, ultimately approximating the predicted signal sequence[2].
In system modeling, adaptive filters are used as models to estimate the characteristics of unknown systems. In communication channel equalization, adaptive equalization is particularly important. For example, high-speed modems need to adjust the coefficient to account for the diversity of channels to minimize the effects of channel distortion[3]. For digital communication receivers, adaptive filters are responsible for channel identification, providing equalization for intercode crosstalk and ensuring stable and efficient data transmission.
Adaptive filter processing technology can be used to detect stationary and non-stationary random signals. The adaptive digital system has a strong ability for self-learning and self-tracking. The algorithm is simple and easy to implement. Developed in the early 1960s, it is closely related to information theory, detection and best estimation theory, as well as filtering theory, and it represents an important branch of signal processing[4]. With the rapid development of VLSI technology and computer technology, along with continuous improvements in adaptive filter theory, its application is becoming increasingly widespread. It has been widely used in communication, speech signal processing, image processing, pattern recognition, system recognition and automatic control, serving as one of the most active fields at present. The adaptive filter has a wide range of applications, mainly in five aspects: adaptive filter and inverse filter; aystem identification; adaptive equalization: adaptive echo cancellation; noise cancellation in communication.
3 Adaptive filter in high-fidelity audio signal processing
3.1 Application Fields
The adaptive filter not only improves the audio quality and fidelity, but also adapts to different audio environments and efficiently processes complex audio signals in high-fidelity audio signal processing. This flexibility makes it a key tool in the field of audio processing, especially in changing application scenarios[5].
3.2 Noise elimination
In a variety of audio environments, adaptive filtering technology can automatically identify and eliminate background noise, such as ambient noise or electromagnetic interference[6]. This real-time noise suppression function plays a crucial role in improving the auditory experience and the clarity of the audio signal, especially when applied in noisy environments.
3.3 Echo elimination
Adaptive filters can be used in a variety of communication and recording environments. By precisely adjusting filter parameters, echoes of different frequencies and intensification can be effectively eliminated. Especially in teleconferencing or live recording, echo cancellation technology ensures the purity of sound and the effectiveness of communication, enhancing the overall audio experience.
3.4 Spectrum line enhancement
Adaptive filters can dynamically adjust audio signals in specific frequency bands, enhancing critical spectral lines in music or speech. This not only improves the artistic performance of the audio, but also helps tuners and sound engineers optimize the layers and details of the sound in professional audio processing scenarios, ultimately creating a more pleasing final output.
3.5 System Identification
In complex audio environments, adaptive filters can identify specific sound sources or sound patterns by analyzing the characteristics of audio signals algorithmically[7]. This technology is particularly important in areas such as security monitoring and speech recognition, which not only improves the accuracy of recognition, but also shows excellent adaptability in dynamic changing environments.
3.6 Other Applications
The flexibility of adaptive filters makes them promising in a variety of audio processing applications, especially in scenarios where different audio features need to be balanced[8]. Through continuous optimization of adaptive algorithms, its application in high-fidelity audio processing will continue to expand, and promote the development of audio technology.
3.7 Audio compression
In audio compression technology, adaptive filters are dynamically adjusted to effectively compress audio data while preserving key audio details. This concept is applicable to the transfer, storage, and streaming of audio files, ensuring a high-quality audio experience while maintaining a small file size.
3.8 Active noise control
The application of an adaptive filter in an active noise control system can generate a signal that is opposite to the phase of ambient noise in real time to realize effective noise cancellation. [9]. This technology is widely used in high-end headsets, cars and aircraft cabins to provide users with a quieter and more comfortable environment. Additionally, it helps reduce auditory fatigue caused by prolonged exposure to noise.
3.9 Dynamic range control
In dynamic range control, adaptive filters can be adjusted in real-time based on changes in the intensity of the audio signal, ensuring that the sound remains clear at different volumes[10]. This is especially important in radio, film production and music production to ensure that listeners enjoy the best sound quality experience at any volume.
3.10 Acoustic feedback suppression
In real-time audio systems, acoustic feedback is a common problem that affects sound quality. Adaptive filters can effectively prevent whistling by quickly identifying and suppressing feedback signals. This technology is widely used in performances, meeting rooms and other scenes to ensure the stability and clarity of audio output, while improving the safety of the use of equipment[11].
3.11 Room balance
The application of an adaptive filter in a room equalization system can adjust the audio output in real time to adapt to the acoustic characteristics of different rooms[12]. By dynamically compensating the room's resonant frequencies and sound wave reflections, listeners can experience consistent and high-quality audio in a variety of environments, which is especially important for high-end home theater and professional recording studios.
4 Status quo of high-fidelity audio signal processing
A switch is used in adaptive filtering to better handle high-noise data. The principle is to decompose the row wavelet to get the wavelet coefficients at all levels. Then the wavelet coefficients of each layer are input into the adaptive filter, and the corresponding filter output is obtained[13]. Finally, the output of each filter is reconstructed by wavelet, and the final filtering result is obtained.
The algorithm applies the particle swarm optimization algorithm to the adaptive filter in order to converge and optimize the filter parameters faster. The principle is to first design the fitness function and then calculate the fitness value based on the error between the filter parameters and the expected output. After this, each particle in the particle swarm is initialized, and the filter parameters are randomly generated. Then the speed and position of each particle in the particle swarm are iteratively updated by the particle swarm optimization algorithm, and the fitness value is calculated. Finally, when the specified number of iterations or the fitness value reaches a certain value, the iteration is stopped and the optimal filter parameters are output[14].
Finally, the IMS(least mean square) algorithm and RIS(recursive least square) algorithm are used to realize its function. IMS algorithm is a recursive calculation method, which updates the weight obtained by iterative calculation. The RIS algorithm is a recursive regression algorithm, which updates the weights by calculating the inverse matrix of the filter.
Different algorithms in high-fidelity audio processing have their own characteristics, and the choice of the appropriate algorithm depends on the specific application scenario and demand. The rule-based algorithm is suitable for high-precision recognition in specific scenarios, the isolated word recognition algorithm is suitable for fast matching of specific words, and the statistical speech recognition algorithm is suitable for large-scale data processing requiring high precision.
There are regular speech recognition algorithms. It mainly relies on pre-defined rules to match the speech signal obtained from the recording. It has high accuracy, but the development and maintenance costs are relatively high and can only be applied to fixed scenarios, such as OTC teller machines. The limitation of this algorithm is that it needs to write different rules for different speech scenarios, so its application scope is relatively limited.
The second is the isolated word recognition algorithm. By analyzing and processing different characteristic parameters of the speech signal, such as the frequency, time domain and frequency domain of the speech, all possible speech signals in the dictionary are enumerated. The speech entry closest to the current speech signal is then selected as the final result. This method provides high accuracy and is suitable for recognizing specific words due to its limited vocabulary.
Finally, the statistical speech recognition algorithm is able to predict speech, intonation, and other features with a high level of accuracy. This is achieved through the analysis of a large number of speech data samples, allowing the algorithm to learn and improve its predictive capabilities.[15]. However, due to the large amount of data required for training, it is relatively expensive to develop and implement.
5 Conclusion
It is worth applying more artificial intelligence to high-fidelity audio signal processing. Audio noise reduction, translation and synthesis algorithms can be used to provide new approaches. The audio noise reduction algorithm based on a deep learning model can more accurately restore the original quality of the audio signal, and the audio translation synthesis algorithm can automatically convert audio into text or generate high-quality synthetic sound through natural language processing and speech recognition technology. These algorithms do an excellent job of improving the efficiency and accuracy of audio processing.
Undoubtedly, achieving a balance between signal adaptation and computational complexity poses the greatest challenges in honing one's skills. Adaptive filtering technology must be capable of rapidly adapting to changes in the audio environment in order to uphold the quality of the signal. For instance, in a voice communication system, if there is a sudden increase in background noise, an adaptive filter needs to be able to adjust quickly to minimize the impact of noise on voice quality. Besides, computational complexity is an important consideration when pursuing high-fidelity audio signal processing. Complex algorithms may lead to a large amount of computing resource consumption, which affects the performance of real-time processing. This can pose a constraint for real-time or online audio signal processing applications, particularly in environments with limited resources. For example, recursive least squares (RLS) algorithms can better adapt to signal changes, but their computational complexity is high and may not be suitable for real-time applications or have limited computational resources. The ratio of signal quality to resource use can be maximized by making the best compromise.
At last, the deep learning method has great potential and application prospects in the signal processing of adaptive filters, which can improve the performance and adaptability of adaptive filters and promote the development of signal processing technology. In the future, with the continuous progress and improvement of deep learning technology, it is believed that more adaptive filter applications based on deep learning will be seen in the field of signal processing, bringing new breakthroughs and innovations to signal processing technology.
References
[1]. Yin, L., Zhang, Z., Wu, M., Wang, Z., Ma, C., Zhou, S., & Yang, J. (2023). Adaptive parallel filter method for active cancellation of road noise inside vehicles. Mechanical Systems and Signal Processing, 193, 110274.
[2]. Yang, R., Peng, Y., & Hu, X. (2023). A fast high-fidelity source-filter vocoder with lightweight neural modules. IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[3]. Yu, G., Zheng, X., Li, N., Han, R., Zheng, C., Zhang, C., ... & Yu, B. (2024, April). BAE-Net: A Low complexity and high fidelity Bandwidth-Adaptive neural network for speech super-resolution. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 571-575). IEEE.
[4]. Yoneyama, R., Wu, Y. C., & Toda, T. (2023). High-fidelity and pitch-controllable neural vocoder based on unified source-filter networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[5]. Gupta, A., Hoffmann, P. F., Prepelitǎ, S., Robinson, P., Ithapu, V. K., & Alon, D. L. (2023, June). Learning to Personalize Equalization for High-Fidelity Spatial Audio Reproduction. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
[6]. Ahn, S., Woo, B. J., Han, M. H., Moon, C., & Kim, N. S. (2024). HILCodec: High Fidelity and Lightweight Neural Audio Codec. arxiv preprint arxiv:2405.04752.
[7]. Lu, Y. X., Ai, Y., & Ling, Z. H. (2023, May). Check for updates Source-Filter-Based Generative Adversarial Neural Vocoder for High Fidelity Speech Synthesis. In Man-Machine Speech Communication: 17th National Conference, NCMMSC 2022, Hefei, China, December 15–18, 2022, Proceedings (p. 68). Springer Nature.
[8]. Yoneyama, R., Yamamoto, R., & Tachibana, K. (2023, June). Nonparallel high-quality audio super resolution with domain adaptation and resampling CycleGANs. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
[9]. Wang, C., Zeng, C., Chen, J., & Xue, O. (2024, July). HiFi-WaveGAN: Generative adversarial network with auxiliary spectrogram-phase loss for high-fidelity singing voice generation. In International Symposium on Neural Networks (pp. 80-92). Singapore: Springer Nature Singapore.
[10]. Lee, S. H., Choi, H. Y., & Lee, S. W. (2024). Accelerating High-Fidelity Waveform Generation via Adversarial Flow Matching Optimization. arxiv preprint arxiv:2408.08019.
[11]. Morrison, M., Churchwell, C., Pruyne, N., & Pardo, B. (2024). Fine-grained and interpretable neural speech editing. arxiv preprint arxiv:2407.05471.
[12]. Churchwell, C., Morrison, M., & Pardo, B. (2024). High-fidelity neural phonetic posteriorgrams. arxiv preprint arxiv:2402.17735.
[13]. Zhang, J., Zhang, X., Sun, M., Zou, X., Jia, C., & Li, Y. (2024). Target speaker filtration by mask estimation for source speaker traceability in voice conversion. Engineering Applications of Artificial Intelligence, 136, 109071.
[14]. Khan, M. (2024). Application of Distributed Arithmetic to Adaptive Filtering Algorithms: Trends, Challenges and Future. arxiv preprint arxiv:2403.08099.
[15]. Matsubara, K., Okamoto, T., Takashima, R., Takiguchi, T., Toda, T., & Kawai, H. (2023). Harmonic-Net: Fundamental frequency and speech rate controllable fast neural vocoder. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1902-1915.
Cite this article
Huang,Z. (2024). A Review of the Challenges of Adaptive Filtering Technology in High-fidelity Audio Signal Processing. Theoretical and Natural Science,55,61-66.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Yin, L., Zhang, Z., Wu, M., Wang, Z., Ma, C., Zhou, S., & Yang, J. (2023). Adaptive parallel filter method for active cancellation of road noise inside vehicles. Mechanical Systems and Signal Processing, 193, 110274.
[2]. Yang, R., Peng, Y., & Hu, X. (2023). A fast high-fidelity source-filter vocoder with lightweight neural modules. IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[3]. Yu, G., Zheng, X., Li, N., Han, R., Zheng, C., Zhang, C., ... & Yu, B. (2024, April). BAE-Net: A Low complexity and high fidelity Bandwidth-Adaptive neural network for speech super-resolution. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 571-575). IEEE.
[4]. Yoneyama, R., Wu, Y. C., & Toda, T. (2023). High-fidelity and pitch-controllable neural vocoder based on unified source-filter networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[5]. Gupta, A., Hoffmann, P. F., Prepelitǎ, S., Robinson, P., Ithapu, V. K., & Alon, D. L. (2023, June). Learning to Personalize Equalization for High-Fidelity Spatial Audio Reproduction. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
[6]. Ahn, S., Woo, B. J., Han, M. H., Moon, C., & Kim, N. S. (2024). HILCodec: High Fidelity and Lightweight Neural Audio Codec. arxiv preprint arxiv:2405.04752.
[7]. Lu, Y. X., Ai, Y., & Ling, Z. H. (2023, May). Check for updates Source-Filter-Based Generative Adversarial Neural Vocoder for High Fidelity Speech Synthesis. In Man-Machine Speech Communication: 17th National Conference, NCMMSC 2022, Hefei, China, December 15–18, 2022, Proceedings (p. 68). Springer Nature.
[8]. Yoneyama, R., Yamamoto, R., & Tachibana, K. (2023, June). Nonparallel high-quality audio super resolution with domain adaptation and resampling CycleGANs. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
[9]. Wang, C., Zeng, C., Chen, J., & Xue, O. (2024, July). HiFi-WaveGAN: Generative adversarial network with auxiliary spectrogram-phase loss for high-fidelity singing voice generation. In International Symposium on Neural Networks (pp. 80-92). Singapore: Springer Nature Singapore.
[10]. Lee, S. H., Choi, H. Y., & Lee, S. W. (2024). Accelerating High-Fidelity Waveform Generation via Adversarial Flow Matching Optimization. arxiv preprint arxiv:2408.08019.
[11]. Morrison, M., Churchwell, C., Pruyne, N., & Pardo, B. (2024). Fine-grained and interpretable neural speech editing. arxiv preprint arxiv:2407.05471.
[12]. Churchwell, C., Morrison, M., & Pardo, B. (2024). High-fidelity neural phonetic posteriorgrams. arxiv preprint arxiv:2402.17735.
[13]. Zhang, J., Zhang, X., Sun, M., Zou, X., Jia, C., & Li, Y. (2024). Target speaker filtration by mask estimation for source speaker traceability in voice conversion. Engineering Applications of Artificial Intelligence, 136, 109071.
[14]. Khan, M. (2024). Application of Distributed Arithmetic to Adaptive Filtering Algorithms: Trends, Challenges and Future. arxiv preprint arxiv:2403.08099.
[15]. Matsubara, K., Okamoto, T., Takashima, R., Takiguchi, T., Toda, T., & Kawai, H. (2023). Harmonic-Net: Fundamental frequency and speech rate controllable fast neural vocoder. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1902-1915.