A music genre is a classification of music that identifies and arranges music into different types. Music in older days was labeled into different music genres manually which was time consuming and inefficient. However, with an advancement of technology and various researches in Music Information Retrieval (MIR), some form of automation has been seen in the field of music genre categorization. Out of various techniques, Convolutional Neural Network (CNN) has been used to classify music into ten genres: disco, reggae, rock, pop, blue, country, jazz, classical, metal and hip-hop. Digital Signal Processing techniques using Fast Fourier Transform (FFT), Short Time Fourier Transform (SFT) and Mel-Frequency Cepstral Coefficient (MFCC) have been used to generate feature values which are then fed into the classifier developed using CNN. The training and testing of the system has been performed successfully obtaining an accuracy of 71.35% which seems to be significant in MIR. GTZAN Music Dataset, a popular Western music dataset prepared for music analysis, has been used during training and testing; hence, this system works well only with Western music files.