Acoustic Scene Classification Using Reduced MobileNet Architecture

Sounds are ubiquitous in our daily lives, for instance, sounds of vehicles or sounds of conversations between people. Therefore, it is easy to collect all these soundtracks and categorize them into different groups. By doing so, we can use these assets to recognize the scene. Acoustic scene classification allows us to do so by training our machine which can further be installed on devices such as smartphones. This provides people with convenience which improves our lives. Our goal is to maximize our validation rate of our machine learning results and also optimize our usage of hardware. We utilize the dataset from IEEE Detection and Classification of Acoustic Scenes and Events (DCASE) to train our machine. The data of DCASE 2017 contains 15 different kinds of outdoor audio recordings, including beach, bus, restaurant etc. In this work, we use two different types of signal processing techniques which are Log Mel and HPSS (Harmonic-Percussive Sound Separation). Next we modify and reduce the MobileNet structure to train our dataset. We also make use of fine-tuning and late fusion to make our results more accurate and to improve our performances. With the structure aforementioned, we succeed in reaching the validation rate of 75.99% which is approximately the seventh highest performing group of the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2017, with less computational complexity comparing with others having higher accuracy. We deem it a worthy trade-off.