Abstract: This paper describes an automated implementable
system for impulsive signals detection and recognition. The system
uses a Digital Signal Processing device for the detection and
identification process. Here the system analyses the signals in real
time in order to produce a particular response if needed. The system
analyses the signals in real time in order to produce a specific output
if needed. Detection is achieved through normalizing the inputs and
comparing the read signals to a dynamic threshold and thus avoiding
detections linked to loud or fluctuating environing noise.
Identification is done through neuronal network algorithms. As a
setup our system can receive signals to “learn” certain patterns.
Through “learning” the system can recognize signals faster, inducing
flexibility to new patterns similar to those known. Sound is captured
through a simple jack input, and could be changed for an enhanced
recording surface such as a wide-area recorder. Furthermore a
communication module can be added to the apparatus to send alerts
to another interface if needed.
Abstract: The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.