Automatic Speech/Music classification uses different signal processing
techniques to categorize multimedia content into different classes. The
proposed work explores Hilbert Spectrum (HS) obtained from different AM-FM
components of an audio signal, also called Intrinsic Mode Functions (IMFs)
to classify an incoming audio signal into speech/music signal. The HS is a
twodimensional representation of instantaneous energies (IE) and
instantaneous frequencies (IF) obtained using Hilbert Transform of the IMFs.
This HS is further processed using Mel-filter bank and Discrete Cosine
Transform (DCT) to generate novel IF and Instantaneous Amplitude (IA) based
cepstral features. Validations of the results were done using three
databases-Slaney Database, GTZAN and MUSAN database. To evaluate the
general applicability of the proposed features, extensive experiments were
conducted on different combination of audio files from S&S, GTZAN and MUSAN
database and promising results are achieved. Finally, performance of the
system is compared with performance of existing cepstral features and
previous works in this domain.
The automatic face recognition attendance system was designed with the aim of marking attendance of the students present in a classroom based on facial recognition and give out a marked attendance sheet.The system provides an efficient way of marking and storing the attendance without having to physically call out the name of each student. It helps save time and the attendance shall be directly stored without having to maintain a physical record.
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