Audio Content based Geotagging in Multimedia
June 09, 2016 ยท Declared Dead ยท ๐ Interspeech
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Anurag Kumar, Benjamin Elizalde, Bhiksha Raj
arXiv ID
1606.02816
Category
cs.SD: Sound
Cross-listed
cs.MM
Citations
6
Venue
Interspeech
Last Checked
3 months ago
Abstract
In this paper we propose methods to extract geographically relevant information in a multimedia recording using its audio. Our method primarily is based on the fact that urban acoustic environment consists of a variety of sounds. Hence, location information can be inferred from the composition of sound events/classes present in the audio. More specifically, we adopt matrix factorization techniques to obtain semantic content of recording in terms of different sound classes. These semantic information are then combined to identify the location of recording.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Sound
๐ฎ
๐ฎ
The Ethereal
R.I.P.
๐ป
Ghosted
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
R.I.P.
๐ป
Ghosted
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
R.I.P.
๐ป
Ghosted
TasNet: time-domain audio separation network for real-time, single-channel speech separation
R.I.P.
๐ป
Ghosted
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
R.I.P.
๐ป
Ghosted
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted