11 accepted papers
Semi-supervised singing melody extraction (SSME) is one of the key tasks in the field of music information retrieval (MIR). Recently, several SSME methods have been proposed and achieved remarkable successes. However, existing methods are still facing two critical issues: firstly, there is a lack of
Singing melody extraction (SME) is a key task in the field of music information retrieval. However, existing methods are facing several limitations: firstly, prior models use transformers to capture the contextual dependencies, which requires quadratic computation resulting in low efficiency in the…
Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex problems. However, existing benchmarks for evaluating LLMs’ tool usage face several limitations: (1) limited evaluation
Function calling significantly extends the application boundary of large language models (LLMs), where high-quality and diverse training data is critical for unlocking this capability. However, collecting and annotating real function-calling data is challenging, while synthetic data from existing pi…
Singing melody extraction serves as an important foundation in the realm of music information retrieval (MIR). Although fully convolutional neural networks (CNNs) are commonly employed for singing melody extraction, they are constrained by inductive biases and face challenges in establishing long ra…
Extracting the melody of a singing voice is an essential task within the realm of music information retrieval (MIR). Recently, transformer based models have drawn great attention in the field of MIR. However, due to the expensive computation cost and extensive parameters, it is difficult to train an…
Singing melody extraction is an important task in the field of music information retrieval (MIR). The development of data-driven models for this task have achieved great successes. However, the existing models have two major limitations: firstly, most of the existing singing melody extraction model…
A plethora of great successes has been achieved by the existing convolutional neural networks (CNN) for respiratory sound classification. Nevertheless, simultaneously capturing both the local and global features can never be an easy task due to the limitation of a CNN’s structure. In this contributi…
Singing melody extraction from polyphonic music is a critical and challenging task in music information retrieval (MIR). However, due to the interfere of the accompaniment and the background noise, it is key and challenging to obtain a global semantic representation that discriminates the singing me…
Singing melody extraction is an important problem in the field of music information retrieval. Existing methods typically rely on frequency-domain representations to estimate the sung frequencies. However, this design does not lead to human-level performance in the perception of melody information f…
Musical audio is generally composed of three physical properties: frequency, time and magnitude. Interestingly, human auditory periphery also provides neural codes for each of these dimensions to perceive music. Inspired by these intrinsic characteristics, a frequency-temporal attention network is p…