  Large-scale datasets have played a significant role in progress of neural
network and deep learning areas. YouTube-8M is such a benchmark dataset for
general multi-label video classification. It was created from over 7 million
YouTube videos (450,000 hours of video) and includes video labels from a
vocabulary of 4716 classes (3.4 labels/video on average). It also comes with
pre-extracted audio & visual features from every second of video (3.2 billion
feature vectors in total). Google cloud recently released the datasets and
organized 'Google Cloud & YouTube-8M Video Understanding Challenge' on Kaggle.
Competitors are challenged to develop classification algorithms that assign
video-level labels using the new and improved Youtube-8M V2 dataset. Inspired
by the competition, we started exploration of audio understanding and
classification using deep learning algorithms and ensemble methods. We built
several baseline predictions according to the benchmark paper and public github
tensorflow code. Furthermore, we improved global prediction accuracy (GAP) from
base level 77% to 80.7% through approaches of ensemble.
