DCASE 2019 Task 2: Multitask Learning, Semi-supervised Learning and Model Ensemble with Noisy Data for Audio Tagging

This paper describes our approach to the DCASE 2019 challenge Task 2: Audio tagging with noisy labels and minimal supervision. This task is a multi-label audio classification with 80 classes. The training data is composed of a small amount of reliably labeled data (curated data) and a larger amount of data with unreliable labels (noisy data). Additionally, there is a difference in data distribution between curated data and noisy data. To tackle this difficulty, we propose three strategies. The first is multitask learning using noisy data. The second is semi-supervised learning using noisy data and labels that are relabeled using trained models’ predictions. The third is an ensemble method that averages models trained with different time length. By using these methods, our solution was ranked in 3rd place on the public leaderboard (LB) with a label-weighted label-ranking average precision (lwlrap) score of 0.750 and ranked in 4th place on the private LB with a lwlrap score of 0.75787. The code of our solution is available at https://github.com/OsciiArt/Freesound-Audio-Tagging-2019.252

Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications

An approach of image coding for tag recommendation based on feature clustering and weighted coding is presented in this paper. The existing tag recommendation approach develops a decision based on correlation of image features and their tag annotated. The descriptive feature of the image sample defines the content of an image and is correlated with database features for tag recommendation. The feature dimension and its representation have a greater impact on the recommendation performance. The recent method tag recommendation developed CNN based visual features and proposed a tag recommendation based on weight factor. The dimensional feature and the isolated weight allocation limit the performance of presented tag recommendation system. This paper presents a new weight allocation and feature clustering method for tag recommendation. An approach of integral coding for weighted image-tag is presented to improve recommendation accuracy. The proposed recommendation system performance is tested on Flickr dataset for retrieval and recommendation accuracy