Abstract: The present disclosure describes techniques for melody extraction. The techniques comprise receiving a polyphonic symbolic music file. The polyphonic symbolic music file may comprise a plurality of notes. The polyphonic symbolic music file may be converted to a plurality of feature vectors. Each of the plurality of feature vectors may be a multidimensional vector. Each of the plurality of feature vectors may correspond to a particular note of the plurality of notes. The plurality of feature vectors corresponding to the plurality of notes may be classified using a model that is trained to determine whether each of the plurality of notes belongs to a melody based on the plurality of feature vectors.

Abstract: Various embodiments of the present disclosure are directed towards a semiconductor package comprising optically coupled integrated circuit (IC) chips. A first IC chip and a second IC chip overlie a substrate at a center of the substrate. A photonic chip overlies the first and second IC chips and is electrically coupled to the second IC chip. A laser device chip overlies the substrate, adjacent to the photonic chip and the second IC chip, at a periphery of the substrate. The photonic chip is configured to modulate a laser beam from the laser device chip in accordance with an electrical signal from the second IC chip and to provide the modulated laser beam to the first IC chip. This facilitates optical communication between the first IC chip to the second IC chip. Various embodiments of the present disclosure are further directed towards simultaneously aligning and bonding constituents of the semiconductor package.

Abstract: Devices, systems and methods related to causing an apparatus to generate music information of audio data using a transformer-based neural network model with a multilevel transformer for audio analysis, using a spectral and a temporal transformer, are disclosed herein.

Abstract: Devices, systems and methods related to causing an apparatus to generate music information of audio data using a transformer-based neural network model with a multilevel transformer for audio analysis, using a spectral and a temporal transformer, are disclosed herein.

Abstract: Devices, systems, and methods related to implementing supervised metric learning during a training of a deep neural network model are disclosed herein. In examples, audio input may be received, where the audio input includes a plurality of song fragments from a plurality of songs. For each song fragment, an aligning function may be performed to center the song fragment based on determined beat information, thereby creating a plurality of aligned song fragments. For each song fragment of the plurality of song fragments, an embedding vector may be obtained from the deep neural network. Thus, a batch of aligned song fragments from the plurality of aligned song fragments may be selected, such that a training tuple may be selected. A loss metric may be generated based on the selected training tuple and one or more weights of the deep neural network model may be updated based on the loss metric.