IndoWaveSentiment: Indonesian Audio Dataset for Emotion Classification

IndoWaveSentiment is an audio dataset designed for classifying emotional expressions in Indonesian speech. The dataset was created using recordings from 10 actors, equally split between men and women. Recordings were made using a mono channel cardioid vocal microphone positioned no more than 10 cm from the speakers, connected to a laptop or computer. The audio was captured at a sample rate of 16 kHz with 32-bit depth. Each actor repeated the same sentence three times across five emotional categories: neutral, happy, surprised, disgusted, and disappointed. The dataset comprises 300 .wav files in total. This data was utilized to develop deep learning models as an initial study on audio classification in multimodal sentiment analysis. IndoWaveSentiment is also suitable for various signal processing applications, including voice emotion classification, and supports the development of sentiment analysis.