@patent{wang2024supervisedmetric,
    Author={Wang, Ju-Chiang and Smith, Jordan B. L. and Lu, Wei-Tsung},
    Title={Supervised metric learning for music structure features},
    Number={12106740},
    Nationality={United States},
    Holder={Lemon Inc Cayman Island}
    Date={2024-10-01},
    Date-filed={2021-10-15},
    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.}
}