Sistema adaptativo de cancelación de ruido usando filtros de mínimos cuadrados

In this paper, we show an Adaptive Noise Canceller (ANC) that estimate an original audio a signal measured with noise. Adaptive system is implemented using a Recursive Least Squares filter (RLS). Its design parameters consider the filter order, forgetting factor and initial conditions to obtain optimal coefficients through iterations. A medium square error (MSE) around to 10-6  is reached, and with this it makes possible a low-cost implementation.En este artículo, se presenta el diseño de un sistema adaptativo de cancelación de ruido, en el cual se estima una señal medida con ruido. Se implementa este sistema adaptativo utilizando un filtro de mínimos cuadrados (RLS). Este diseño considera parámetros tales como el orden del filtro, factor de olvido y condiciones iniciales para obtener los coeficientes óptimos del filtro a través de iteraciones. Se logró un error cuadrático medio alrededor de 10-6 , y con esto es posible hacer una implementación de bajo costo

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

Adaptive techniques for signal enhancement in the human electroencephalogram

This thesis describes an investigation of adaptive noise cancelling applied to human brain evoked potentials (EPs), with particular emphasis on visually evoked responses. The chief morphological features and signal properties of EPs are described. Consideration is given to the amplitude and spectral properties of the underlying spontaneous electroencephalogram and the importance of noise reduction techniques in EP studies is empnasised. A number of methods of enhancing EP waveforms are reviewed in the light of the known limitations of coherent signal averaging. These are shown to oe generally inadequate for enhancing individual EP responses. The theory of adaptive filters is reviewed with particular reference to adaptive transversal filters usiny the Widrow-Hoff algorithm. The theory of adaptive noise cancelling using correlated reference sources is presented, and new work is described which relates canceller performance to the magnitude-squared coherence function of the input signals. A novel filter structure, the gated adaptive filter, is presented and shown to yield improved cancellation without signal distortion when applied to repetitive transient signals in stationary noise under the condition of fast adaption. The signal processing software available is shown to be inadequate, and a comprehensive Fortran program developed for use on a PDP-11 computer is described. The properties of human visual evoked potentials and the EEO are investigated in two normal adults using a montage of 7 occipital electrodes. Signal enhancement of EPs is shown to be possible oy adaptive noise cancelling, and improvements in signal to noise in the range 2-10 dB are predicted. A discussion of filter strategies is presented, and a detailed investiyation of adaptive noise cancel liny performed usiny a ranye of typical EP data. Assessment of the results confirms the proposal that substantial improvement in sinyle EP response recoynition is achieved by this technique

Reducing Microphone Artefacts in Live Sound

PhDThis thesis presents research into reducing microphone artefacts in live sound with no prior knowledge of the sources or microphones. Microphone artefacts are defined as additional sounds or distortions that occur on a microphone signal that are often undesired. We focus on the proximity effect, comb filtering and microphone bleed. In each case we present a method that either automatically implements human sound engineering techniques or we present a novel method that makes use of audio signal processing techniques that goes beyond the skills of a sound engineer. By doing this we can show that a higher quality mix of a live performance can be achieved. Firstly we investigate the proximity effect which occurs on directional microphones. We present a method for detecting the proximity effect with no prior knowledge of the source to microphone distance. This then leads to a method for reducing the proximity effect which employs a dynamic filter informed by audio analysis. Comb filtering occurs when the output of microphones reproducing the same source are mixed together. We present a novel analysis of how the accuracy of a technique to automatically estimate the correct delay of the source between each microphone is affected by source bandwidth and the windowing function applied to the data. We then present a method for reducing microphone bleed in the multiple source, multiple microphone case, both in determined and overdetermined configurations. The proposed method is extended from prior research in noise cancellation, which has not previously been applied to musical sound sources. We then present a method for simulating microphone bleed in synthesised drum recordings, where bleed enhances the realism of the output. Through subjective listening tests and objective measures each proposed method is shown to succeed at reducing the microphone artefacts while preserving the original sound source.EPSR

Biomedical signal filtering for noisy environments

 Luke\u27s work addresses issue of robustly attenuating multi-source noise from surface EEG signals using a novel Adaptive-Multiple-Reference Least-Means-Squares filter (AMR-LMS). In practice, the filter successfully removes electrical interference and muscle noise generated during movement which contaminates EEG, allowing subjects to maintain maximum mobility throughout signal acquisition and during the use of a Brain Computer Interface