Comparison of Neural Networks and Least Mean Squared Algorithms for Active Noise Canceling

Active Noise Canceling (ANC) is the idea of using superposition to achieve cancellation of unwanted noise and is implemented for many applications such as attempting to reduce noise in a commercial airplane cabin. One of the main traditional techniques for noise cancellation is the adaptive least mean squares (LMS) algorithm that produces the anti-noise signal, or the 180 degree out-of-phase signal to cancel the noise via superposition. This work attempts to compare several neural network approaches against the traditional LMS algorithms. The noise signals that are used for the training of the network are from the Signal Processing Information Base (SPIB) database. The neural network architectures utilized in this paper include the Multilayer Feedforward Neural Network, the Recurrent Neural Network, the Long Short Term Neural Network, and the Convolutional Neural Network. These neural networks are trained to predict the anti-noise signal based on an incoming noise signal. The results of the simulation demonstrate successful ANC using neural networks, and they show that neural networks can yield better noise attenuation than LMS algorithms. Results show that the Convolutional Neural Network architecture outperforms the other architectures implemented and tested in this work

Control of feedback for assistive listening devices

Acoustic feedback refers to the undesired acoustic coupling between the loudspeaker and microphone in hearing aids. This feedback channel poses limitations to the normal operation of hearing aids under varying acoustic scenarios. This work makes contributions to improve the performance of adaptive feedback cancellation techniques and speech quality in hearing aids. For this purpose a two microphone approach is proposed and analysed; and probe signal injection methods are also investigated and improved upon

Spatial Noise-Field Control With Online Secondary Path Modeling: A Wave-Domain Approach

Due to strong interchannel interference in multichannel active noise control (ANC), there are fundamental problems associated with the filter adaptation and online secondary path modeling remains a major challenge. This paper proposes a wave-domain adaptation algorithm for multichannel ANC with online secondary path modelling to cancel tonal noise over an extended region of two-dimensional plane in a reverberant room. The design is based on exploiting the diagonal-dominance property of the secondary path in the wave domain. The proposed wave-domain secondary path model is applicable to both concentric and nonconcentric circular loudspeakers and microphone array placement, and is also robust against array positioning errors. Normalized least mean squares-type algorithms are adopted for adaptive feedback control. Computational complexity is analyzed and compared with the conventional time-domain and frequency-domain multichannel ANCs. Through simulation-based verification in comparison with existing methods, the proposed algorithm demonstrates more efficient adaptation with low-level auxiliary noise.DP14010341

Adaptive frequency domain identification for ANC systems using non-stationary signals

The problem of identification of secondary path in active noise control applications is dealt with fundamentally using time-domain adaptive filters. The use of adaptive frequency domain subband identification as an alternative has some significant advantages which are overlooked in such applications. In this paper two different delayless subband adaptive algorithms for identification of an unknown secondary path in an ANC framework are utilized and compared. Despite of reduced computational complexity and increase convergence rate this approach allows us to use non-stationary audio signals as the excitation input to avoid injection of annoying white noise. For this purpose two non-stationary music and speech signals are used for identification. The performances of the algorithms are measured in terms of minimum mean square error and convergence speed. The results are also compared to a fullband algorithm for the same scenario. The proposed delayless algorithms have a closed loop structure with DFT filterbanks as the analysis filter. To eliminate the delay in the signal path two different weights transformation schemes are compared

Adaptive signal processing for multichannel sound using high performance computing

[EN] The field of audio signal processing has undergone a major development in recent years. Both the consumer and professional marketplaces continue to show growth in audio applications such as immersive audio schemes that offer optimal listening experience, intelligent noise reduction in cars or improvements in audio teleconferencing or hearing aids. The development of these applications has a common interest in increasing or improving the number of discrete audio channels, the quality of the audio or the sophistication of the algorithms. This often gives rise to problems of high computational cost, even when using common signal processing algorithms, mainly due to the application of these algorithms to multiple signals with real-time requirements. The field of High Performance Computing (HPC) based on low cost hardware elements is the bridge needed between the computing problems and the real multimedia signals and systems that lead to user's applications. In this sense, the present thesis goes a step further in the development of these systems by using the computational power of General Purpose Graphics Processing Units (GPGPUs) to exploit the inherent parallelism of signal processing for multichannel audio applications. The increase of the computational capacity of the processing devices has been historically linked to the number of transistors in a chip. However, nowadays the improvements in the computational capacity are mainly given by increasing the number of processing units and using parallel processing. The Graphics Processing Units (GPUs), which have now thousands of computing cores, are a representative example. The GPUs were traditionally used to graphic or image processing, but new releases in the GPU programming environments such as CUDA have allowed the use of GPUS for general processing applications. Hence, the use of GPUs is being extended to a wide variety of intensive-computation applications among which audio processing is included. However, the data transactions between the CPU and the GPU and viceversa have questioned the viability of the use of GPUs for audio applications in which real-time interaction between microphones and loudspeakers is required. This is the case of the adaptive filtering applications, where an efficient use of parallel computation in not straightforward. For these reasons, up to the beginning of this thesis, very few publications had dealt with the GPU implementation of real-time acoustic applications based on adaptive filtering. Therefore, this thesis aims to demonstrate that GPUs are totally valid tools to carry out audio applications based on adaptive filtering that require high computational resources. To this end, different adaptive applications in the field of audio processing are studied and performed using GPUs. This manuscript also analyzes and solves possible limitations in each GPU-based implementation both from the acoustic point of view as from the computational point of view.[ES] El campo de procesado de señales de audio ha experimentado un desarrollo importante en los últimos años. Tanto el mercado de consumo como el profesional siguen mostrando un crecimiento en aplicaciones de audio, tales como: los sistemas de audio inmersivo que ofrecen una experiencia de sonido óptima, los sistemas inteligentes de reducción de ruido en coches o las mejoras en sistemas de teleconferencia o en audífonos. El desarrollo de estas aplicaciones tiene un propósito común de aumentar o mejorar el número de canales de audio, la propia calidad del audio o la sofisticación de los algoritmos. Estas mejoras suelen dar lugar a sistemas de alto coste computacional, incluso usando algoritmos comunes de procesado de señal. Esto se debe principalmente a que los algoritmos se suelen aplicar a sistemas multicanales con requerimientos de procesamiento en tiempo real. El campo de la Computación de Alto Rendimiento basado en elementos hardware de bajo coste es el puente necesario entre los problemas de computación y los sistemas multimedia que dan lugar a aplicaciones de usuario. En este sentido, la presente tesis va un paso más allá en el desarrollo de estos sistemas mediante el uso de la potencia de cálculo de las Unidades de Procesamiento Gráfico (GPU) en aplicaciones de propósito general. Con ello, aprovechamos la inherente capacidad de paralelización que poseen las GPU para procesar señales de audio y obtener aplicaciones de audio multicanal. El aumento de la capacidad computacional de los dispositivos de procesado ha estado vinculado históricamente al número de transistores que había en un chip. Sin embargo, hoy en día, las mejoras en la capacidad computacional se dan principalmente por el aumento del número de unidades de procesado y su uso para el procesado en paralelo. Las GPUs son un ejemplo muy representativo. Hoy en día, las GPUs poseen hasta miles de núcleos de computación. Tradicionalmente, las GPUs se han utilizado para el procesado de gráficos o imágenes. Sin embargo, la aparición de entornos sencillos de programación GPU, como por ejemplo CUDA, han permitido el uso de las GPU para aplicaciones de procesado general. De ese modo, el uso de las GPU se ha extendido a una amplia variedad de aplicaciones que requieren cálculo intensivo. Entre esta gama de aplicaciones, se incluye el procesado de señales de audio. No obstante, las transferencias de datos entre la CPU y la GPU y viceversa pusieron en duda la viabilidad de las GPUs para aplicaciones de audio en las que se requiere una interacción en tiempo real entre micrófonos y altavoces. Este es el caso de las aplicaciones basadas en filtrado adaptativo, donde el uso eficiente de la computación en paralelo no es sencillo. Por estas razones, hasta el comienzo de esta tesis, había muy pocas publicaciones que utilizaran la GPU para implementaciones en tiempo real de aplicaciones acústicas basadas en filtrado adaptativo. A pesar de todo, esta tesis pretende demostrar que las GPU son herramientas totalmente válidas para llevar a cabo aplicaciones de audio basadas en filtrado adaptativo que requieran elevados recursos computacionales. Con este fin, la presente tesis ha estudiado y desarrollado varias aplicaciones adaptativas de procesado de audio utilizando una GPU como procesador. Además, también analiza y resuelve las posibles limitaciones de cada aplicación tanto desde el punto de vista acústico como desde el punto de vista computacional.[CA] El camp del processament de senyals d'àudio ha experimentat un desenvolupament important als últims anys. Tant el mercat de consum com el professional segueixen mostrant un creixement en aplicacions d'àudio, com ara: els sistemes d'àudio immersiu que ofereixen una experiència de so òptima, els sistemes intel·ligents de reducció de soroll en els cotxes o les millores en sistemes de teleconferència o en audiòfons. El desenvolupament d'aquestes aplicacions té un propòsit comú d'augmentar o millorar el nombre de canals d'àudio, la pròpia qualitat de l'àudio o la sofisticació dels algorismes que s'utilitzen. Això, sovint dóna lloc a sistemes d'alt cost computacional, fins i tot quan es fan servir algorismes comuns de processat de senyal. Això es deu principalment al fet que els algorismes se solen aplicar a sistemes multicanals amb requeriments de processat en temps real. El camp de la Computació d'Alt Rendiment basat en elements hardware de baix cost és el pont necessari entre els problemes de computació i els sistemes multimèdia que donen lloc a aplicacions d'usuari. En aquest sentit, aquesta tesi va un pas més enllà en el desenvolupament d'aquests sistemes mitjançant l'ús de la potència de càlcul de les Unitats de Processament Gràfic (GPU) en aplicacions de propòsit general. Amb això, s'aprofita la inherent capacitat de paral·lelització que posseeixen les GPUs per processar senyals d'àudio i obtenir aplicacions d'àudio multicanal. L'augment de la capacitat computacional dels dispositius de processat ha estat històricament vinculada al nombre de transistors que hi havia en un xip. No obstant, avui en dia, les millores en la capacitat computacional es donen principalment per l'augment del nombre d'unitats de processat i el seu ús per al processament en paral·lel. Un exemple molt representatiu són les GPU, que avui en dia posseeixen milers de nuclis de computació. Tradicionalment, les GPUs s'han utilitzat per al processat de gràfics o imatges. No obstant, l'aparició d'entorns senzills de programació de la GPU com és CUDA, han permès l'ús de les GPUs per a aplicacions de processat general. D'aquesta manera, l'ús de les GPUs s'ha estès a una àmplia varietat d'aplicacions que requereixen càlcul intensiu. Entre aquesta gamma d'aplicacions, s'inclou el processat de senyals d'àudio. No obstant, les transferències de dades entre la CPU i la GPU i viceversa van posar en dubte la viabilitat de les GPUs per a aplicacions d'àudio en què es requereix la interacció en temps real de micròfons i altaveus. Aquest és el cas de les aplicacions basades en filtrat adaptatiu, on l'ús eficient de la computació en paral·lel no és senzilla. Per aquestes raons, fins al començament d'aquesta tesi, hi havia molt poques publicacions que utilitzessin la GPU per implementar en temps real aplicacions acústiques basades en filtrat adaptatiu. Malgrat tot, aquesta tesi pretén demostrar que les GPU són eines totalment vàlides per dur a terme aplicacions d'àudio basades en filtrat adaptatiu que requereixen alts recursos computacionals. Amb aquesta finalitat, en la present tesi s'han estudiat i desenvolupat diverses aplicacions adaptatives de processament d'àudio utilitzant una GPU com a processador. A més, aquest manuscrit també analitza i resol les possibles limitacions de cada aplicació, tant des del punt de vista acústic, com des del punt de vista computacional.Lorente Giner, J. (2015). Adaptive signal processing for multichannel sound using high performance computing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/58427TESI

Adaptive Algorithms Design for Active Noise Control Systems with Disturbance at Reference and Error Microphones

Active noise control (ANC) is a popular choice for mitigating the acoustic noise in the surrounding environment resulting from industrial and medical equipment, appliances, and consumer electronics. ANC cancels the low frequency acoustic noise by generating a cancelling sound from speakers. The speakers are triggered by noise control filters and produce sound waves with the same amplitude and inverted phase to the original sound. Noise control filters are updated by adaptive algorithms. Successful applications of this technology are available in headsets, earplugs, propeller aircraft, cars and mobile phones. Since multiple applications are running simultaneously, efficiency of the adaptive control algorithms in terms of implementation, computations and performance is critical to the performance of the ANC systems. The focus of the present project is on the development of efficient adaptive algorithms that perform optimally in different configurations of ANC systems suitable for real world applications.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 202

ANCシステムにおけるオンライン2次経路とフィードバック経路モデリングのための補助ノイズ電力スケジューリングに関する研究

The idea of cancelling the acoustic noise by generating an anti-noise signal is very fascinating, and was first proposed by P. Lueg in 1936. In feedforward active noise control (ANC) systems, the anti-noise signal is generated with the help of reference and error microphones, an adaptive filtered-x-LMS (FxLMS) algorithm based ANC filter, and an electro-acoustic path named as the secondary path. For stable operation of ANC systems, the FxLMS algorithm needs an estimate of the secondary path. The anti-noise signal generated by the loudspeaker (part of secondary path) causes interference with the reference microphone signal. This interference is due to the presence of electro-acoustic path, named as feedback path, between the loudspeaker and the reference microphone. It is required to neutralize the effect of this feedback path, and hence an estimate of the feedback path is required. For online modeling of the secondary and feedback paths, an additional auxiliary noise is injected. This auxiliary noise contributes to the residual error, and thus degrades the noise-reduction-performance (NRP) of ANC system. In order to improve the NRP, a gain scheduling strategy is used to vary the variance of the injected auxiliary noise. The purpose of the gain scheduling is that when the model estimates of the secondary and the feedback paths are far from the actual unknown paths, auxiliary noise with large variance is injected. Once the model estimates are closer to the actual unknown paths, the variance of auxiliary noise is reduced to a small value. In this way, on one hand the gain scheduling can help us to achieve the required model estimates of secondary and feedback paths, and on the other hand to improve the NRP at the steady-state. In this thesis, we discuss the two most important issues, i.e., 1) online secondary path modeling (OSPM), and 2) online feedback path modeling and neutralization (FBPMN) with gain scheduling. In chapter 1, the basic underlying physical principle and configurations of active noise control (ANC) systems are explained. The application of the basic building block of an ANC system i.e. An adaptive filter, in different system identification scenarios is discussed. The most popular adaptive algorithm for ANC system, i.e., FxLMS algorithm is derived for the general secondary path. A brief overview is given for the two fundamental issues in ANC systems, i.e., 1) OSPM and 2) online FBPMN. The use of optimal excitation signal, i.e., Perfect sweep signals for system identification is described. In chapter 2, the existing methods for OSPM without gain scheduling, where the auxiliary noise with fixed variance is used in all operating conditions, are discussed. In this chapter a simplified structure for OSPM with the modified FxLMS (MFxLMS) adaptive algorithm is proposed. The advantage of the simplified structure is that it reduces the computational complexity of the MFxLMS algorithm based OSPM without having any compromise on the performance of ANC system. In chapter 3, the existing methods for OSPM with gain scheduling are discussed. The drawbacks with the existing gain scheduling strategies are highlighted, and some new gain scheduling strategies are proposed to improve the modeling accuracy of SPM filter and the NRP of an ANC system. In existing methods, the gain is varied based on the power of residual error signal which carries information only about the convergence status of ANC system. In the Proposed methods the gain is varied based on the power of error signal of SPM filter. This is more desirable way of controlling the gain because the power of error signal of SPM filter carries information about the convergence status of both the ANC system and the SPM filter. The performance comparison is carried out through the simulation results. In chapter 4, the second most important issue associated with the feedforward configuration of ANC system, i.e., the issue of online FBPMN is deal with. In the first part, the existing methods for online FBPMN without gain scheduling are discussed. A new structure is proposed for online FBPMN without gain scheduling. The performance of the existing methods is compare with the proposed method through the simulation results. In the new structure the good features from the existing structures are combined together. The predictor is used in the new structure to remove the predictable interference term from the error signal of adaptive FBPMN filter. In addition to this, the action of FBPM filter and the FBPN filter is combined into a single FBPMN filter. The advantage of the new structure over the existing structures is that it can better neutralize the effect of feedback coupling on the input signal of ANC filter, thus improves the convergence of ANC system. In the second part, a gain scheduling strategy is proposed to improve the NRP of ANC system. In addition to this, a self-tuned ANP scheduling strategy with matching step-size for FBPMN filter is also proposed that requires no tuning parameters and further improves the NRP of ANC systems. In chapter 5, the concluding remarks and some future research directions are given.電気通信大学201