The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.
Recent researches on parallelization of H.264 video decoders focused on fine-grain methods. These works led to designs having very short latencies and good memory usage. However, they could not reach the scalability of Group of Pictures (GOP) level approaches although assuming a welldesigned entropy decoder which can feed the increasing number of parallel working cores. We would like to introduce a GOP-level approach due to its high scalability, mentioning solution approaches for the well-known latency and memory issues. Our design revokes the need to a scanner for GOP startcodes which was used in the earlier methods. This approach lets all the cores work on the decoding task. Although the performance on shared memory systems is subject to improve, we have observed a one-to-one linear speedup in parallel working nodes. We have tested our method using a cluster of 5 machines each having 2 processors with 4 cores. The decoding is 5 times faster when we run only one process in each machine, that is we saw one-to-one linear speedup when there is no memory shortage. We observed a maximum of 11 times speedup when using all of the 40 cores distributed among 5 machines.
Acceleration feedback techniques have been used in control systems for a long time in order to improve the stiffness. Basically, the acceleration of the motor is calculated using speed or position measurements and the current command is adjusted using the acceleration data for avoiding deviations from the commanded speed. This method has to deal with two challenges. The first challenge is calculating the acceleration correctly. If it is calculated by double differentiation of position feedback of a servo motor, it may lead to a high amount of noise. On the other hand, if observer techniques are used, the error and variation in system parameters and quantization noise in the measured current may lead to incorrect calculation of the motor acceleration. The second challenge is avoiding performance degradation in the transient response of the system and preventing oscillations. Since acceleration feedback will try to avoid any acceleration in the system, it may severely affect the transient-state performance. This paper presents a disturbance rejection method that does not depend on system parameters and that does not affect the transient-state response of the system. The method provides very significant improvement in disturbance rejection over a wide frequency range. The magnitude of disturbance response was -29.6 dB in the original scheme at its peak frequency of 5.17 Hz; using the proposed method, it improved to -44.7 dB at the same frequency.
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