1,598 research outputs found
Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units
Audio tagging aims to detect the types of sound events occurring in an audio
recording. To tag the polyphonic audio recordings, we propose to use
Connectionist Temporal Classification (CTC) loss function on the top of
Convolutional Recurrent Neural Network (CRNN) with learnable Gated Linear Units
(GLU-CTC), based on a new type of audio label data: Sequentially Labelled Data
(SLD). In GLU-CTC, CTC objective function maps the frame-level probability of
labels to clip-level probability of labels. To compare the mapping ability of
GLU-CTC for sound events, we train a CRNN with GLU based on Global Max Pooling
(GLU-GMP) and a CRNN with GLU based on Global Average Pooling (GLU-GAP). And we
also compare the proposed GLU-CTC system with the baseline system, which is a
CRNN trained using CTC loss function without GLU. The experiments show that the
GLU-CTC achieves an Area Under Curve (AUC) score of 0.882 in audio tagging,
outperforming the GLU-GMP of 0.803, GLU-GAP of 0.766 and baseline system of
0.837. That means based on the same CRNN model with GLU, the performance of CTC
mapping is better than the GMP and GAP mapping. Given both based on the CTC
mapping, the CRNN with GLU outperforms the CRNN without GLU.Comment: DCASE2018 Workshop. arXiv admin note: text overlap with
arXiv:1808.0193
Sound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering
Sound event detection (SED) methods typically rely on either strongly
labelled data or weakly labelled data. As an alternative, sequentially labelled
data (SLD) was proposed. In SLD, the events and the order of events in audio
clips are known, without knowing the occurrence time of events. This paper
proposes a connectionist temporal classification (CTC) based SED system that
uses SLD instead of strongly labelled data, with a novel unsupervised
clustering stage. Experiments on 41 classes of sound events show that the
proposed two-stage method trained on SLD achieves performance comparable to the
previous state-of-the-art SED system trained on strongly labelled data, and is
far better than another state-of-the-art SED system trained on weakly labelled
data, which indicates the effectiveness of the proposed two-stage method
trained on SLD without any onset/offset time of sound events
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language
Processing, Special Issue on Sound Scene and Event Analysi
Joint Multi-Pitch Detection Using Harmonic Envelope Estimation for Polyphonic Music Transcription
In this paper, a method for automatic transcription of music signals based on joint multiple-F0 estimation is proposed. As a time-frequency representation, the constant-Q resonator time-frequency image is employed, while a novel noise suppression technique based on pink noise assumption is applied in a preprocessing step. In the multiple-F0 estimation stage, the optimal tuning and inharmonicity parameters are computed and a salience function is proposed in order to select pitch candidates. For each pitch candidate combination, an overlapping partial treatment procedure is used, which is based on a novel spectral envelope estimation procedure for the log-frequency domain, in order to compute the harmonic envelope of candidate pitches. In order to select the optimal pitch combination for each time frame, a score function is proposed which combines spectral and temporal characteristics of the candidate pitches and also aims to suppress harmonic errors. For postprocessing, hidden Markov models (HMMs) and conditional random fields (CRFs) trained on MIDI data are employed, in order to boost transcription accuracy. The system was trained on isolated piano sounds from the MAPS database and was tested on classic and jazz recordings from the RWC database, as well as on recordings from a Disklavier piano. A comparison with several state-of-the-art systems is provided using a variety of error metrics, where encouraging results are indicated
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