187 research outputs found
Learning Local Feature Aggregation Functions with Backpropagation
This paper introduces a family of local feature aggregation functions and a
novel method to estimate their parameters, such that they generate optimal
representations for classification (or any task that can be expressed as a cost
function minimization problem). To achieve that, we compose the local feature
aggregation function with the classifier cost function and we backpropagate the
gradient of this cost function in order to update the local feature aggregation
function parameters. Experiments on synthetic datasets indicate that our method
discovers parameters that model the class-relevant information in addition to
the local feature space. Further experiments on a variety of motion and visual
descriptors, both on image and video datasets, show that our method outperforms
other state-of-the-art local feature aggregation functions, such as Bag of
Words, Fisher Vectors and VLAD, by a large margin.Comment: In Proceedings of the 25th European Signal Processing Conference
(EUSIPCO 2017
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
We study large-scale kernel methods for acoustic modeling and compare to DNNs
on performance metrics related to both acoustic modeling and recognition.
Measuring perplexity and frame-level classification accuracy, kernel-based
acoustic models are as effective as their DNN counterparts. However, on
token-error-rates DNN models can be significantly better. We have discovered
that this might be attributed to DNN's unique strength in reducing both the
perplexity and the entropy of the predicted posterior probabilities. Motivated
by our findings, we propose a new technique, entropy regularized perplexity,
for model selection. This technique can noticeably improve the recognition
performance of both types of models, and reduces the gap between them. While
effective on Broadcast News, this technique could be also applicable to other
tasks.Comment: arXiv admin note: text overlap with arXiv:1411.400
AXES at TRECVid 2011
The AXES project participated in the interactive known-item search task (KIS) and the interactive instance search task (INS) for TRECVid 2011. We used the same system architecture and a nearly identical user interface for both the KIS and INS tasks. Both systems made use of text search on ASR, visual concept detectors, and visual similarity search. The user experiments were carried out with media professionals and media students at the Netherlands Institute for Sound and Vision, with media professionals performing the KIS task and media students participating in the INS task. This paper describes the results and findings of our experiments
Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording
In this paper we present our work on Task 1 Acoustic Scene Classi- fication
and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments
we have low-level and high-level features, classifier optimization and other
heuristics specific to each task. Our performance for both tasks improved the
baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9%
compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based
Error Rate of 0.76 compared to the baseline of 0.91
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