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Found 11 papers, 1 papers with code
Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity
Automatic Audio Captioning (AAC) refers to the task of translating an audio sample into a natural language (NL) text that describes the audio events, source of the events and their relationships.
Spectro Temporal EEG Biomarkers For Binary Emotion Classification
The usefulness of these features for EEG emotion classification is investigated through extensive experiments using state-of-the-art classifiers.
Automatic Audio Captioning using Attention weighted Event based Embeddings
Automatic Audio Captioning (AAC) refers to the task of translating audio into a natural language that describes the audio events, source of the events and their relationships.
A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios
In this paper, we propose a novel three step technique to address imbalanced data.
Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
In this paper, we propose to replace the typical prototypical loss function with an Episodic Triplet Mining (ETM) technique.
Identification of Dementia Using Audio Biomarkers
The objective of this work is to use speech processing and machine learning techniques to automatically identify the stage of dementia such as mild cognitive impairment (MCI) or Alzheimers disease (AD).
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications
Naturally introduced perturbations in audio signal, caused by emotional and physical states of the speaker, can significantly degrade the performance of Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities
Multimodal sentiment classification in practical applications may have to rely on erroneous and imperfect views, namely (a) language transcription from a speech recognizer and (b) under-performing acoustic views.
Automatic Speech Recognition (ASR)
General Classification
+2
A Novel Approach for Effective Learning in Low Resourced Scenarios
Deep learning based discriminative methods, being the state-of-the-art machine learning techniques, are ill-suited for learning from lower amounts of data.
k-FFNN: A priori knowledge infused Feed-forward Neural Networks
It is not immediately clear (a) how a priori temporal knowledge can be used in a FFNN architecture (b) how a FFNN performs when provided with this knowledge about temporal correlations (assuming available) during training.
