Objective Prediction of the Sound Quality of Music Processed by an Adaptive Feedback Canceller

Manders, A.; Simpson, David M.; Bell, Steven L.

doi:10.1109/tasl.2012.2188513

Cited by 16 publications

(2 citation statements)

References 23 publications

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“…• Signal to Distortion Ratio: The signal to distortion ratio (SDR) compares the levels of nonlinear distortion in the processed signal to the original signal, and is based on the Hilbert transform [48], [49], [50].…”

Section: B Performance Measuresmentioning

confidence: 99%

Prediction Error Method (PEM)-Based Howling Cancellation in Hearing Aids: Can We Do Better?

2023

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This work develops an effective technique acoustic feedback cancellation (AFC) in the digital hearing aid (DHAid) devices. The normalized least mean square (NLMS) algorithm-based AFC method may suffer from a biased convergence. The biased convergence problem is considerably resolved by the prediction error method (PEM)-based AFC (PEM-AFC); however, it may demonstrate a slow convergence. The proposed method's main structure is based two adaptive filters. The main adaptive AFC filter receives its input from the DHAid receiver signal, while the auxiliary AFC filter is activated by a probe signal. The main idea is to apply a lattice filtering-based pre-processing for decorrelation in the main AFC filter's update equation. This produces a Newton-like adaptive algorithm with fast convergence. Additionally, the lattice filtering is executed on a sample-by-sample basis, in contrast to the frame-based execution in the traditional PEM-AFC method. As the AFC system converges, the level of the probe signal is decreased to improve the output SNR; however, the low-level input signal slows down auxiliary AFC filter's convergence. In order to improve the convergence speed, the gradient information from a maximum Versoria-criterion (MVC) is incorporated into the auxiliary AFC filter's update algorithm. The two adaptive filters' coefficients are exchanged, to ensure that both adaptive filters converge to a good estimate of the true acoustic feedback path. Simulations show that the proposed method works well for speech/signals and for DHAid devices with different gain settings. Additionally, the proposed method shows robust performance in the event of a sudden change in the acoustic environment.INDEX TERMS Normalized LMS algorithm, lattice adaptive filtering, digital hearing aid devices, acoustic feedback cancellation.

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Section: B Performance Measuresmentioning

confidence: 99%

Prediction Error Method (PEM)-Based Howling Cancellation in Hearing Aids: Can We Do Better?

2023

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“…3 In addition, recent papers have shown high degrees of correlation for perceptual metrics used to predict quality ratings for feedback cancellation in HAs. 11,12 However, the idea of using HASQI as an objective metric for optimizing AFC is novel.…”

Section: Hasqimentioning

confidence: 99%

Speech quality and stable gain trade-offs in adaptive feedback cancellation for hearing aids

Lee

Kates

Rao

et al. 2017

The Journal of the Acoustical Society of America

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This paper addresses trade-offs in adaptive feedback cancellation (AFC) for hearing aids. Aggressive AFC for improved added stable gain (ASG) reduces speech quality. In this paper, the hearing-aid speech quality index (HASQI) is used to investigate AFC performance before the system becomes unstable. It is demonstrated that for a desired speech quality, multiple AFC algorithms can be evaluated for their ASG and computational efficiency. An example is presented with HASQI ¼ 0.8, baseline AFC, and two advanced approaches. For the advanced AFCs, ASG gains of 4 and 7 dB were obtained at additional computational complexity of 8% and 11%, respectively.

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Review of Existing Objective QoE Methodologies

Fang

Wang

2015

Multimedia Quality of Experience (QoE)

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Objective Prediction of the Sound Quality of Music Processed by an Adaptive Feedback Canceller

Cited by 16 publications

References 23 publications

Prediction Error Method (PEM)-Based Howling Cancellation in Hearing Aids: Can We Do Better?

Prediction Error Method (PEM)-Based Howling Cancellation in Hearing Aids: Can We Do Better?

Speech quality and stable gain trade-offs in adaptive feedback cancellation for hearing aids

Review of Existing Objective QoE Methodologies

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