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The Collaborative Research Centre SFB 1330 Hearing acoustics: Perceptual principles, Algorithms and Applications (HAPPAA) at the Carl von Ossietzky Universität Oldenburg is seeking to fill the position of a
Research Associate (m/f/d)
The Collaborative Research Centre SFB 1330 Hearing acoustics: Perceptual principles, Algorithms and Applications (HAPPAA) at the Carl von Ossietzky Universität Oldenburg is seeking to fill the position of a
Research Scientist / PhD Student in Acoustical Signal Processing (m/f/d)
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
Many well-known line spectral estimators may experience significant performance loss with noisy measurements. To address the problem, we propose a deep learning denoising based approach for line spectral estimation. The proposed approach utilizes a residual learning assisted denoising convolutional neural network (DnCNN) trained to recover the unstructured noise component, which is used to denoise the original measurements.
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
Two-directional two-dimensional canonical correlation analysis ((2D) 2 CCA) directly seeks linear relationship between different image data sets without reshaping images into vectors. However, it fails in finding the nonlinear correlation.
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
The multiple signal classification (MUSIC) algorithmis computationally expensive in the application to joint two-dimensional (2-D) direction-of-arrival (DOA) and time-of-arrival (TOA) estimation based on uniform circular array (UCA) using orthogonal frequency-division multiplexing (OFDM) signal. This letter proposed an efficient way to compute the 3-D spatial-temporal spectrum.
Section: Publications & Resources
A sound field reproduction method based on the spherical wavefunction expansion of sound fields is proposed, which can be flexibly applied to various array geometries and directivities. First, we formulate sound field synthesis as a minimization problem of some norm on the difference between the desired and synthesized sound fields, and then the optimal driving signals are derived by using the spherical wavefunction expansion of the sound fields.
Section: Publications & Resources
Sequence generation tasks, such as neural machine translation (NMT) and abstractive summarization, usually suffer from exposure bias as well as the error propagation problem due to the autoregressive training and generation. Many previous works have discussed the relationship between error propagation and the accuracy drop problem (i.e., the right part of the generated sentence is often worse than its left part in left-to-right decoding models).
Section: Publications & Resources
The acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion is a natural end-to-end (E2E) system directly targeting word as output unit. Two issues exist in the system: first, the current output of the CTC model relies on the current input and does not account for context weighted inputs. This is the hard alignment issue.
Issue Date:
Section: Community & Involvement
IEEE SPS Speech and Language Processing Technical Committee Nominations 2019
The Member Election Subcommittee of the SLTC is seeking nominations for new SLTC Members for a 3-year term (2020-2022). Nominations should
be submitted by filling out the web form at https://forms.gle/sm9pStwFFK63zWem8. The nomination deadline is October 20th, 2019.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
In a typical communication pipeline, images undergo a series of processing steps that can cause visual distortions before being viewed. Given a high quality reference image, a reference (R) image quality assessment (IQA) algorithm can be applied after compression or transmission. However, the assumption of a high quality reference image is often not fulfilled in practice, thus contributing to less accurate quality predictions when using stand-alone R IQA models.
