Active vibration control systems in the frequency and sub-band domain.

Active noise and vibration control has been the subject of intense study in the last two decades due to the increased speed in digital signal processors and the technological development and manufacture of smart materials. This dissertation analyzes an active control system using adaptive digital signal processing techniques and applies it to the vibration reduction of hard disk drives (HDD). Specifically, this work focuses on the implementation of the adaptive algorithm in the frequency and sub-band domains for performance improvement.In this dissertation, selective adaptation in the frequency domain is proposed to alleviate the constructive interference associated with a feedback active control system. A new sub-band adaptive filter architecture without a signal path delay is proposed, and the associated adaptive algorithm is derived. This delayless sub-band algorithm can be applied to the active control systems to improve the convergence rate and trade-off the performance from the various sub-bands. The resulting side effect of the error path delay of the analysis filter bank is analyzed, and two compensation methods are proposed to increase the performance. The frequency domain method and the sub-band decomposition technique are then combined to improve the overall performance. The single-channel active control system is extended to the multiple-channel active control system to reduce the vibration of complex mechanical structure. The optimal performances of three variants of the feedback control system have been derived in terms of the correlation coefficients of the primary disturbances and the impulse responses of the secondary paths. Real time and simulation results are performed to verify the efficiency of the proposed algorithms and techniques

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

Doctor of Philosophy

dissertationHearing aids suffer from the problem of acoustic feedback that limits the gain provided by hearing aids. Moreover, the output sound quality of hearing aids may be compromised in the presence of background acoustic noise. Digital hearing aids use advanced signal processing to reduce acoustic feedback and background noise to improve the output sound quality. However, it is known that the output sound quality of digital hearing aids deteriorates as the hearing aid gain is increased. Furthermore, popular subband or transform domain digital signal processing in modern hearing aids introduces analysis-synthesis delays in the forward path. Long forward-path delays are not desirable because the processed sound combines with the unprocessed sound that arrives at the cochlea through the vent and changes the sound quality. In this dissertation, we employ a variable, frequency-dependent gain function that is lower at frequencies of the incoming signal where the information is perceptually insignificant. In addition, the method of this dissertation automatically identifies and suppresses residual acoustical feedback components at frequencies that have the potential to drive the system to instability. The suppressed frequency components are monitored and the suppression is removed when such frequencies no longer pose a threat to drive the hearing aid system into instability. Together, the method of this dissertation provides more stable gain over traditional methods by reducing acoustical coupling between the microphone and the loudspeaker of a hearing aid. In addition, the method of this dissertation performs necessary hearing aid signal processing with low-delay characteristics. The central idea for the low-delay hearing aid signal processing is a spectral gain shaping method (SGSM) that employs parallel parametric equalization (EQ) filters. Parameters of the parametric EQ filters and associated gain values are selected using a least-squares approach to obtain the desired spectral response. Finally, the method of this dissertation switches to a least-squares adaptation scheme with linear complexity at the onset of howling. The method adapts to the altered feedback path quickly and allows the patient to not lose perceivable information. The complexity of the least-squares estimate is reduced by reformulating the least-squares estimate into a Toeplitz system and solving it with a direct Toeplitz solver. The increase in stable gain over traditional methods and the output sound quality were evaluated with psychoacoustic experiments on normal-hearing listeners with speech and music signals. The results indicate that the method of this dissertation provides 8 to 12 dB more hearing aid gain than feedback cancelers with traditional fixed gain functions. Furthermore, experimental results obtained with real world hearing aid gain profiles indicate that the method of this dissertation provides less distortion in the output sound quality than classical feedback cancelers, enabling the use of more comfortable style hearing aids for patients with moderate to profound hearing loss. Extensive MATLAB simulations and subjective evaluations of the results indicate that the method of this dissertation exhibits much smaller forward-path delays with superior howling suppression capability

Blind identification of acoustic systems and enhancement of reverberant speech

Imperial Users onl

Broadband adaptive beamforming with low complexity and frequency invariant response

This thesis proposes different methods to reduce the computational complexity as well as increasing the adaptation rate of adaptive broadband beamformers. This is performed exemplarily for the generalised sidelobe canceller (GSC) structure. The GSC is an alternative implementation of the linearly constrained minimum variance beamformer, which can utilise well-known adaptive filtering algorithms, such as the least mean square (LMS) or the recursive least squares (RLS) to perform unconstrained adaptive optimisation.A direct DFT implementation, by which broadband signals are decomposed into frequency bins and processed by independent narrowband beamforming algorithms, is thought to be computationally optimum. However, this setup fail to converge to the time domain minimum mean square error (MMSE) if signal components are not aligned to frequency bins, resulting in a large worst case error. To mitigate this problem of the so-called independent frequency bin (IFB) processor, overlap-save based GSC beamforming structures have been explored. This system address the minimisation of the time domain MMSE, with a significant reduction in computational complexity when compared to time-domain implementations, and show a better convergence behaviour than the IFB beamformer. By studying the effects that the blocking matrix has on the adaptive process for the overlap-save beamformer, several modifications are carried out to enhance both the simplicity of the algorithm as well as its convergence speed. These modifications result in the GSC beamformer utilising a significantly lower computational complexity compare to the time domain approach while offering similar convergence characteristics.In certain applications, especially in the areas of acoustics, there is a need to maintain constant resolution across a wide operating spectrum that may extend across several octaves. To attain constant beamwidth is difficult, particularly if uniformly spaced linear sensor array are employed for beamforming, since spatial resolution is reciprocally proportional to both the array aperture and the frequency. A scaled aperture arrangement is introduced for the subband based GSC beamformer to achieve near uniform resolution across a wide spectrum, whereby an octave-invariant design is achieved. This structure can also be operated in conjunction with adaptive beamforming algorithms. Frequency dependent tapering of the sensor signals is proposed in combination with the overlap-save GSC structure in order to achieve an overall frequency-invariant characteristic. An adaptive version is proposed for frequency-invariant overlap-save GSC beamformer. Broadband adaptive beamforming algorithms based on the family of least mean squares (LMS) algorithms are known to exhibit slow convergence if the input signal is correlated. To improve the convergence of the GSC when based on LMS-type algorithms, we propose the use of a broadband eigenvalue decomposition (BEVD) to decorrelate the input of the adaptive algorithm in the spatial dimension, for which an increase in convergence speed can be demonstrated over other decorrelating measures, such as the Karhunen-Loeve transform. In order to address the remaining temporal correlation after BEVD processing, this approach is combined with subband decomposition through the use of oversampled filter banks. The resulting spatially and temporally decorrelated GSC beamformer provides further enhanced convergence speed over spatial or temporal decorrelation methods on their own

Development and applications of adaptive IIR and subband filters

Adaptive infinite impulse response (IIR) filter is a challenging research area. Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems. In this study, we consider IIR channel both for system identification and channel equalization purposes. We focus on four different approaches: Least Mean Square (LMS), Recursive Least Square (RLS), Genetic Algorithm (GA) and Subband Adaptive Filter (SAF). ). The performance of conventional LMS and RLS based IIR system identification and channel equalization are found with the help of computer simulations. And also the convergence speed and the ability to locate the global optimum solution using a population based algorithm named Genetic Algorithm is given

Filter Optimization for Personal Sound Zones Systems

[ES] Los sistemas de zonas de sonido personal (o sus siglas en inglés PSZ) utilizan altavoces y técnicas de procesado de señal para reproducir sonidos distintos en diferentes zonas de un mismo espacio compartido. Estos sistemas se han popularizado en los últimos años debido a la amplia gama de aplicaciones que podrían verse beneficiadas por la generación de zonas de escucha individuales. El diseño de los filtros utilizados para procesar las señales de sonido es uno de los aspectos más importantes de los sistemas PSZ, al menos para las frecuencias bajas y medias. En la literatura se han propuesto diversos algoritmos para calcular estos filtros, cada uno de ellos con sus ventajas e inconvenientes. En el presente trabajo se revisan los algoritmos para sistemas PSZ propuestos en la literatura y se evalúa experimentalmente su rendimiento en un entorno reverberante. Los distintos algoritmos se comparan teniendo en cuenta aspectos como el aislamiento acústico entre zonas, el error de reproducción, la energía de los filtros y el retardo del sistema. Además, se estudian estrategias computacionalmente eficientes para obtener los filtros y también se compara su complejidad computacional. Los resultados experimentales obtenidos revelan que las soluciones existentes no pueden ofrecer una complejidad computacional baja y al mismo tiempo un buen rendimiento con baja latencia. Por ello se propone un nuevo algoritmo basado en el filtrado subbanda, y se demuestra experimentalmente que este algoritmo mitiga las limitaciones de los algoritmos existentes. Asimismo, este algoritmo ofrece una mayor versatilidad que los algoritmos existentes, ya que se pueden utilizar configuraciones distintas en cada subbanda, como por ejemplo, diferentes longitudes de filtro o distintos conjuntos de altavoces. Por último, se estudia la influencia de las respuestas objetivo en la optimización de los filtros y se propone un nuevo método en el que se aplica una ventana temporal a estas respuestas. El método propuesto se evalúa experimentalmente en dos salas con diferentes tiempos de reverberación y los resultados obtenidos muestran que se puede reducir la energía de las interferencias entre zonas gracias al efecto de la ventana temporal.[CA] Els sistemes de zones de so personal (o les seves sigles en anglés PSZ) fan servir altaveus i tècniques de processament de senyal per a reproduir sons distints en diferents zones d'un mateix espai compartit. Aquests sistemes s'han popularitzat en els últims anys a causa de l'àmplia gamma d'aplicacions que podrien veure's beneficiades per la generació de zones d'escolta individuals. El disseny dels filtres utilitzats per a processar els senyals de so és un dels aspectes més importants dels sistemes PSZ, particularment per a les freqüències baixes i mitjanes. En la literatura s'han proposat diversos algoritmes per a calcular aquests filtres, cadascun d'ells amb els seus avantatges i inconvenients. En aquest treball es revisen els algoritmes proposats en la literatura per a sistemes PSZ i s'avalua experimentalment el seu rendiment en un entorn reverberant. Els distints algoritmes es comparen tenint en compte aspectes com l'aïllament acústic entre zones, l'error de reproducció, l'energia dels filtres i el retard del sistema. A més, s'estudien estratègies de còmput eficient per obtindre els filtres i també es comparen les seves complexitats computacionals. Els resultats experimentals obtinguts revelen que les solucions existents no poder oferir al mateix temps una complexitat computacional baixa i un bon rendiment amb latència baixa. Per això es proposa un nou algoritme basat en el filtrat subbanda que mitiga aquestes limitacions. A més, l'algoritme proposat ofereix una major versatilitat que els algoritmes existents, ja que en cada subbanda el sistema pot utilitzar configuracions diferents, com per exemple, distintes longituds de filtre o distints conjunts d'altaveus. L'algoritme proposat s'avalua experimentalment en un entorn reverberant, i es mostra com pot mitigar satisfactòriament les limitacions dels algoritmes existents. Finalment, s'estudia la influència de les respostes objectiu en l'optimització dels filtres i es proposa un nou mètode en el que s'aplica una finestra temporal a les respostes objectiu. El mètode proposat s'avalua experimentalment en dues sales amb diferents temps de reverberació i els resultats obtinguts mostren que es pot reduir el nivell d'interferència entre zones grècies a l'efecte de la finestra temporal.[EN] Personal Sound Zones (PSZ) systems deliver different sounds to a number of listeners sharing an acoustic space through the use of loudspeakers together with signal processing techniques. These systems have attracted a lot of attention in recent years because of the wide range of applications that would benefit from the generation of individual listening zones, e.g., domestic or automotive audio applications. A key aspect of PSZ systems, at least for low and mid frequencies, is the optimization of the filters used to process the sound signals. Different algorithms have been proposed in the literature for computing those filters, each exhibiting some advantages and disadvantages. In this work, the state-of-the-art algorithms for PSZ systems are reviewed, and their performance in a reverberant environment is evaluated. Aspects such as the acoustic isolation between zones, the reproduction error, the energy of the filters, and the delay of the system are considered in the evaluations. Furthermore, computationally efficient strategies to obtain the filters are studied, and their computational complexity is compared too. The performance and computational evaluations reveal the main limitations of the state-of-the-art algorithms. In particular, the existing solutions can not offer low computational complexity and at the same time good performance for short system delays. Thus, a novel algorithm based on subband filtering that mitigates these limitations is proposed for PSZ systems. In addition, the proposed algorithm offers more versatility than the existing algorithms, since different system configurations, such as different filter lengths or sets of loudspeakers, can be used in each subband. The proposed algorithm is experimentally evaluated and tested in a reverberant environment, and its efficacy to mitigate the limitations of the existing solutions is demonstrated. Finally, the effect of the target responses in the optimization is discussed, and a novel approach that is based on windowing the target responses is proposed. The proposed approach is experimentally evaluated in two rooms with different reverberation levels. The evaluation results reveal that an appropriate windowing of the target responses can reduce the interference level between zones.Molés Cases, V. (2022). Filter Optimization for Personal Sound Zones Systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18611

Sparseness-controlled adaptive algorithms for supervised and unsupervised system identification

In single-channel hands-free telephony, the acoustic coupling between the loudspeaker and the microphone can be strong and this generates echoes that can degrade user experience. Therefore, effective acoustic echo cancellation (AEC) is necessary to maintain a stable system and hence improve the perceived voice quality of a call. Traditionally, adaptive filters have been deployed in acoustic echo cancellers to estimate the acoustic impulse responses (AIRs) using adaptive algorithms. The performances of a range of well-known algorithms are studied in the context of both AEC and network echo cancellation (NEC). It presents insights into their tracking performances under both time-invariant and time-varying system conditions. In the context of AEC, the level of sparseness in AIRs can vary greatly in a mobile environment. When the response is strongly sparse, convergence of conventional approaches is poor. Drawing on techniques originally developed for NEC, a class of time-domain and a frequency-domain AEC algorithms are proposed that can not only work well in both sparse and dispersive circumstances, but also adapt dynamically to the level of sparseness using a new sparseness-controlled approach. As it will be shown later that the early part of the acoustic echo path is sparse while the late reverberant part of the acoustic path is dispersive, a novel approach to an adaptive filter structure that consists of two time-domain partition blocks is proposed such that different adaptive algorithms can be used for each part. By properly controlling the mixing parameter for the partitioned blocks separately, where the block lengths are controlled adaptively, the proposed partitioned block algorithm works well in both sparse and dispersive time-varying circumstances. A new insight into an analysis on the tracking performance of improved proportionate NLMS (IPNLMS) is presented by deriving the expression for the mean-square error. By employing the framework for both sparse and dispersive time-varying echo paths, this work validates the analytic results in practical simulations for AEC. The time-domain second-order statistic based blind SIMO identification algorithms, which exploit the cross relation method, are investigated and then a technique with proportionate step-size control for both sparse and dispersive system identification is also developed