**3. Audio and music signals classification**

The main classification approaches will be discussed in this section. They can be categorized into three different approaches: (1) time domain approaches, (2) frequency domain approaches, and (3) time-frequency domain approaches. A twolevel music and audio classifier was developed by El-Maleh [61, 62]. He used a combination of long-term features such as the variance, the differential parameters, the zero crossing rate (ZCR), and the time-averages of spectral parameters. Saunders [60] proposed another two-level classifier. His approach was based on the short-time energy (STE) and the average ZCR features. In addition, Matityaho and Furst [63] have developed a neural network based model for classifying music signals. Their model was designed based on human cochlea functional performance.

For audio detection, Hoyt and Wecheler [64] have developed a neural network base model using Fourier transform, Hamming filtering, and a logarithmic function as pre-processing then they applied a simple threshold algorithm for detecting audio, music, wind, traffic or any interfering sound. In addition, to improve the performance, they suggested wavelet transform feature for pre-processing. Their work is much similar to the work done by Matityaho and Furst's [63, 64]. 13 features were examined by Scheirer and Slaney [65]. Some of these features were simple modification of each other's. They also tried combining them in several multidimensional classification forms. From these previous works, the most powerful discrimination features were the STE and the ZCR. Therefore, the STE and the ZCR will be discussed thoroughly. Finally, the common classifiers of the audio and the music signals can be divided into the following approaches:

#### I.**The Time domain algorithms**:

1.The ZCR algorithm [1, 34, 66–77]:


2.The STE [60–65, 78].


6.The HMM (Hidden Markov Model) [83–85].

7.The ANN (Artificial neural networks) [12, 49, 58, 63, 79, 83–120].

8.The Roll-Off Variance [31, 59].
