10 accepted papers
Although many models exist to detect singing voice deepfakes (SingFake), how these models operate, particularly with instrumental accompaniment, is unclear. We investigate how instrumental music affects SingFake detection from two perspectives. To investigate the behavioral effect, we test different…
The primary challenge in accent recognition lies in data scarcity due to the high diversity of accents, which make the collection of large-scale training data for each accent almost impossible in practice. To overcome this challenge, we propose a simple solution that leverages both continuous and di…
We introduce MIR-MLPop, a publicly available multilingual pop music dataset designed for automatic lyrics transcription and lyrics alignment in polyphonic music. The dataset comprises 90 pop music tracks in Mandarin, Cantonese, and Taiwanese Hokkien, with manually annotated time-aligned lyrics with…
Audio-visual synchronization aims to determine whether the mouth movements and speech in the video are synchronized. VocaLiST reaches state-of-the-art performance by incorporating multimodal Transformers to model audio-visual interact information. However, it requires high computing resources, makin…
In this paper, we propose a new dataset named EGDB, that contains transcriptions of the electric guitar performance of 240 tablatures rendered with different tones. Moreover, we benchmark the performance of two well-known transcription models proposed originally for the piano on this dataset, along…
This paper proposes a large-scale dataset for singing transcription, along with some methods for fine-tuning and validating its contents. The dataset is named MIR-ST500, which consists of more than 160,000 notes from 500 pop songs. To create this large-scale dataset, we set some labeling criteria an…
In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be quickly adapted to recognize the new sound event without much fine-tuning. The proposed attentional similarity module c…
Separating two sources from an audio mixture is an important task with many applications. It is a challenging problem since only one signal channel is available for analysis. In this paper, we propose a novel framework for singing voice separation using the generative adversarial network (GAN) with…
This paper proposes an innovative method that aligns a polyphonic audio recording of music to its corresponding symbolic score. In the first step, we perform onset detection and then apply constant Q transform around each onset. A similarity matrix is computed by using a scoring function which evalu…
A new algorithm is proposed for robust principal component analysis with predefined sparsity patterns. The algorithm is then applied to separate the singing voice from the instrumental accompaniment using vocal activity information. To evaluate its performance, we construct a new publicly available…