  Music genre classification has become increasingly critical with the advent
of various streaming applications. Nowadays, we find it impossible to imagine
using the artist's name and song title to search for music in a sophisticated
music app. It is always difficult to classify music correctly because the
information linked to music, such as region, artist, album, or non-album, is so
variable. This paper presents a study on music genre classification using a
combination of Digital Signal Processing (DSP) and Deep Learning (DL)
techniques. A novel algorithm is proposed that utilizes both DSP and DL methods
to extract relevant features from audio signals and classify them into various
genres. The algorithm was tested on the GTZAN dataset and achieved high
accuracy. An end-to-end deployment architecture is also proposed for
integration into music-related applications. The performance of the algorithm
is analyzed and future directions for improvement are discussed. The proposed
DSP and DL-based music genre classification algorithm and deployment
architecture demonstrate a promising approach for music genre classification.
