Digital multimedia steganalysis has attracted wide attention over the past decade. Currently, there are many algorithms for detecting image steganography. However, little research has been devoted to audio steganalysis. Since the statistical properties of image and audio files are quite different, features that are effective in image steganalysis may not be effective for audio. In this article, we design an improved audio steganalytic feature set derived from both the time and Mel-frequency domains for detecting some typical steganography in the time domain, including LSB matching, Hide4PGP, and Steghide. The experiment results, evaluated on different audio sources, including various music and speech clips of different complexity, have shown that the proposed features significantly outperform the existing ones. Moreover, we use the proposed features to detect and further identify some typical audio operations that would probably be used in audio tampering. The extensive experiment results have shown that the proposed features also outperform the related forensic methods, especially when the length of the audio clip is small, such as audio clips with 800 samples. This is very important in real forensic situations.