Estimated values across threads are averaged to obtain a stable and accurate estimate of performance speed. Each thread uses ODTW to estimate the current performance speed of the live music performance. The music tracker includes online dynamic time-warping (ODTW) algorithms working across multiple threads. We are still tackling the problem of generating body movements solely from audio content, but there are many possibilities for future development.įor real-time synchronization, our proposed system incorporates three elements, i.e., a music tracker, a music detector, and a position estimator. These same principles can be applied to other kinds of stringed instruments. In terms of music emotion recognition, since periodic head tilt and upper body motion tend to follow the rhythm and music type, we incorporate rhythm tracking from the audio model and an emotion predictor model to control those aspects of body motion. From this information, patterns for the generated skeleton can be determined. The bowing model has been designed with an audio-based attack detection network, whereas the fingering model computes left-hand position from music pitch. Our proposed model consists of a bowing model for the right hand, a fingering (position) model for the left hand, and a musical emotion (expression) model for the upper body. Instead of employing an end-to-end NN, we are focusing on more interpretable and controllable body movement generation methods. Long-term body rhythms can also be determined by our music emotion recognition model. Using a recording of a violin solo as the input signal, we automatically generate coordinate values of the body joints for a virtual violinist. To generate animated body movement, we have achieved preliminary results based on the motion of a violin player. Our U-Net-based architecture considers convolution kernels via attention or dilation mechanisms to simultaneously process objects of different sizes, such as to identify both short and long musical notes. More specifically, our proposed method simplifies the issue of musical transcription into semantic segmentation in computer vision. Consequently, the training model exhibits enhanced robustness, achieves transposition-invariance, and suppresses the challenging overtone errors usually generated in audio processing. Moreover, we can now superimpose different types of signal representations, allowing convolution kernels in NN to automatically select desired features. Nowadays, deep learning-based systems that simultaneously detect multiple pitch, timing, and instrument types have become possible due to the development of neural networks (NN) in multi-task learning (MTL) approaches. In the past, due to the diversity of signal characteristics and data labels, it was difficult to establish a systematic solution for automatic music analysis. Audio analysis primarily involves automatic music transcription, melody detection, and musical instrument recognition. Our proposed system can be divided into three elements: audio analysis, motion generation, and real-time synchronization.
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