A New Cosine Hyperbolic Window Function-based FIR Filter design for Audio to Spectrogram Conversion

In recent years, deep learning-based audio signal processing is a popular way to extract features from audio signals and make the system learnt about those extracted features and patterns. These features are used for speech recognition, tracking vehicles and different types of audio processing. In many cases, to extract salient features and make the system learnt about those features, conversion of audio signal to spectrogram is a vital step. Spectrograms exhibiting minimum noise and interference, contribute significantly to feature extraction, thereby optimizing the efficiency of the learning system. In this paper, a novel adjustable window function, based on Cosine Hyperbolic Function, is proposed to design Finite Impulse Response (FIR) low-pass filter which can be utilized for reducing noise and interference from the spectrograms. The spectral characteristics of the proposed window function are compared with the state-of-the-art window functions. The performance of the Proposed window-based FIR low-pass filter is assessed with state-of-the-art FIR low-pass filters in terms of reducing noise and interference from spectrograms. Experimental result show that the proposed window-based FIR lowpass filter outperforms the existing methods to eliminate noise and interference from audio to spectrogram conversion.