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Abstract Compressive sensing has attracted considerable attention in automotive radar interference mitigation. However, these algorithms usually cannot be applied directly to commercial automotive radar as most of them are computationally intense. In this paper, we therefore introduce a computationally efficient two-dimensional masked residual updates (2D MRU) compressive sensing framework. By utilizing the sparsity of the beat signal in the frequency domain, the range-Doppler (RD) spectrum can be reconstructed with the help of undistorted samples in the beat signal. Unlike the other schemes, where a 2D signal measurement is vectorized into a 1D signal, the proposed 2D MRU can directly take a 2D signal measurement and reconstruct the corresponding RD spectrum. Furthermore, the 2D MRU framework can be easily integrated into well-known optimization schemes such as basis pursuit, iterative hard thresholding, iterative soft thresholding, orthogonal matching pursuit, and approximate message-passing algorithm. In addition to the standard iterative thresholding algorithms, we propose a novel prior-model-based iterative thresholding method to further reduce the computation time and reconstruction error. Theoretical analysis shows that the proposed framework can successfully reconstruct the RD spectrum with high probability. Moreover, numerical experiments demonstrate the superiority of the proposed framework in terms of computational complexity.

Music is difficult to access for the majority of CI users as the reduced dynamic range and poor spectral resolution in cochlear implants (CI), amongst others constraints, severely impair their auditory perception. The reduction of spectral complexity is therefore a promising means to facilitate music enjoyment for CI listeners. We evaluate a spectral complexity reduction method for music signals based on principal component analysis that enforces spectral sparsity, emphasizes the melody contour and attenuates interfering accompanying voices. To cover a wide range of spectral complexity reduction levels a new experimental design for listening experiments was introduced. It allows CI users to select the preferred level of spectral complexity reduction interactively and in real-time. Ten adult CI recipients with post-lingual bilateral profound sensorineural hearing loss and CI experience of at least 6 months were enrolled in the study. In eight consecutive sessions over a period of 4 weeks they were asked to choose their preferred version out of 10 different complexity settings for a total number of 16 recordings of classical western chamber music. As the experiments were performed in consecutive sessions we also studied a potential long term effect. Therefore, we investigated the hypothesis that repeated engagement with music signals of reduced spectral complexity leads to a habituation effect which allows CI users to deal with music signals of increasing complexity. Questionnaires and tests about music listening habits and musical abilities complemented these experiments. The participants significantly preferred signals with high spectral complexity reduction levels over the unprocessed versions. While the results of earlier studies comprising only two preselected complexity levels were generally confirmed, this study revealed a tendency toward a selection of even higher spectral complexity reduction levels. Therefore, spectral complexity reduction for music signals is a useful strategy to enhance music enjoyment for CI users. Although there is evidence for a habituation effect in some subjects, such an effect has not been significant in general.

We describe a speech enhancement algorithm which leads to significant quality and intelligibility improvements when used as a preprocessor to a low bit rate speech coder. This algorithm was developed in conjunction with the mixed excitation linear prediction (MELP) coder which, by itself, is highly susceptible to environmental noise. The paper presents novel as well as known speech and noise estimation techniques and combines them into a highly effective speech enhancement system. The algorithm is based on short-time spectral amplitude estimation, soft-decision gain modification, tracking of the a priori probability of speech absence, and minimum statistics noise power estimation. Special emphasis is placed on enhancing the performance of the preprocessor in nonstationary noise environments.

A new block-based noise reduction system is proposed which focuses on the preservation of transient sounds like stops or speech onsets. The power level of consonants has been shown to be important for speech intelligibility. In single-channel noise reduction systems, however, these sounds are frequently severely attenuated. The main reasons for this are an insufficient temporal resolution of transient sounds and a delayed tracking of important control parameters. The key idea of the proposed system is the detection of non-stationary input data. Depending on that decision, a pair of spectral analysis-synthesis windows is selected which either provides high temporal or high spectral resolution. Furthermore, the decision-directed approach for the estimation of the a priori SNR is modified so that speech onsets are tracked more quickly without sacrificing performance in stationary signal regions. The proposed solution shows significant improvements in the preservation of stops with an overall system delay (input-output, excluding group delay of noise reduction filter) of only 10 milliseconds.

Una caracterización morfológica precisa de las múltiples clases neuronales del cerebro facilitaría la elucidación de la función cerebral y los cambios funcionales que subyacen a los trastornos neurológicos tales como enfermedades de Parkinson o la Esquizofrenia. El análisis morfológico manual es muy lento y sufre de falta de exactitud porque algunas características de las células no se cuantifican fácilmente. Este artículo presenta una investigación en la clasificación automática de un conjunto de neuronas piramidales de monos jóvenes y adultos, las cuales degradan su estructura morfológica con el envejecimiento. Un conjunto de 21 características se utilizaron para describir su morfología con el fin de identificar las diferencias entre las neuronas. En este trabajo se evalúa el desempeño de cuatro métodos de aprendizaje automático populares en la clasificación de árboles neuronales. Los métodos de aprendizaje de máquinas utilizadas son: máquinas de vectores soporte (SVM), k-vecinos más cercanos (KNN), regresión logística multinomial (MLR) y la red neuronal de propagación hacia atrás (BPNN). Los resultados mostraron las ventajas de MLR y BPNN con respecto a los demás para estos fines. Estos algoritmos de clasificación automáticaofrecen ventajas sobre la clasificación manualbasada en expertos.Mientras que la neurociencia está pasando rápidamente a datos digitales, los principios detrás de los algoritmos de clasificación automática permanecen a menudo inaccesibles para los neurocientíficos, lo que limita las posibilidades de avances.Palabras Clave: neuronas, neuroinformática, aprendizaje automático, clasificadores.

Una caracterización morfológica precisa de las múltiples clases neuronales del cerebro facilitaría la elucidación de la función cerebral y los cambios funcionales que subyacen a los trastornos neurológicos tales como enfermedades de Parkinson o la Esquizofrenia. El análisis morfológico manual es muy lento y sufre de falta de exactitud porque algunas características de las células no se cuantifican fácilmente. Este artículo presenta una investigación en la clasificación automática de un conjunto de neuronas piramidales de monos jóvenes y adultos, las cuales degradan su estructura morfológica con el envejecimiento. Un conjunto de 21 características se utilizaron para describir su morfología con el fin de identificar las diferencias entre las neuronas. En este trabajo se evalúa el desempeño de cuatro métodos de aprendizaje automático populares en la clasificación de árboles neuronales. Los métodos de aprendizaje de máquinas utilizadas son: máquinas de vectores soporte (SVM), k-vecinos más cercanos (KNN), regresión logística multinomial (MLR) y la red neuronal de propagación hacia atrás (BPNN). Los resultados mostraron las ventajas de MLR y BPNN con respecto a los demás para estos fines. Estos algoritmos de clasificación automáticaofrecen ventajas sobre la clasificación manualbasada en expertos.Mientras que la neurociencia está pasando rápidamente a datos digitales, los principios detrás de los algoritmos de clasificación automática permanecen a menudo inaccesibles para los neurocientíficos, lo que limita las posibilidades de avances.Palabras Clave: neuronas, neuroinformática, aprendizaje automático, clasificadores.