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Due to Android's popularity, cybercriminals view it as a lucrative target. Malwares with varying behavior patterns that specifically target user routines are constantly entering the market. Because of this, knowing how to identify different forms of malware is crucial for protecting against it. This paper proposes an audio-based malware family detection approach to achieve this goal. Additionally, several audio-based features have been studied, and numerous feature selection techniques have been employed to identify the most successful features. Experiments were carried out using a data set containing 4000 samples from eight families. The results show that effective malware family classification can be made with a small number of features in the audio domain.