logging in or signing up Sound Detection Dario Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 394 Category: Education License: All Rights Reserved Like it (1) Dislike it (0) Added: January 17, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Sound Detection: Sound Detection Derek Hoiem Rahul Sukthankar (mentor) August 24, 2004Objective: Objective Learn model of sound object from few (10-20) examples and distinguish from all other sounds Examples of sound classes: Gunshots, screams, laughter, car horns, meow, dog bark, etcApplications: Applications “Tell me if you hear a gunshot.” (monitoring) “Get me video clips containing dogs barking.” (search and retrieval) “What’s going on?” (scene understanding) Why its difficult: Why its difficult Sound classes have large variations Sounds are often ambiguous without context Overlaid “noise” obscures sound Sound or not?: Sound or not? Car horn Laser gun Dog bark Which of these sounds are not from their named classes?Previous work: Previous work Sound Classification (Wold 1996, Casey 2001, etc) Categorize short sound clips Reasonable accuracy (5-20% error) Sound Detection (Defaux 2000, Piamsa-nga 1999) Localize and recognize sound objects in long clips Poor performance or assumption of unrealistic conditions (e.g., very quiet background) Detection via Windowed Search: Detection via Windowed Search Long Track Break audio track into short overlapping short clips Clip Classifier Independently classify short clips as object or non-object Return locations of detected sound objectRepresentation: Representation meows phone rings Raw RepresentationClassification Features: Classification Features Diverse feature set: Different sound classes are distinctive in different ways means and standard deviations of power at different frequencies Band-width, peaks, loudness, etc. 138 features in all Classification by Decision Trees: Classification by Decision Trees Try to find simple rules that discriminate object from non-object Each decision is based on a threshold of a feature value Assign confidence based on likelihood of data for object and non-object classes at each leaf node Decision nodes Leaf NodesBoosted Trees: Boosted Trees Problem: One decision tree by itself may not be a great classifier Solution: Use several trees, with each one focusing on the mistakes of previously learned trees Adaboost: Weight training data uniformly Learn a decision tree classifier on weighted data Re-weight data giving more weight to incorrectly classified examples Final classification based on linear combination of confidences from all learned decision trees Examples of Decision Trees: Examples of Decision Trees Low percentage of power in low frequencies in mid-time of sound Very high power amplitude range Meow Gunshot High power amplitude range More complex tree that focuses on examples misclassified by tree above GunshotCascade of Classifiers: Cascade of Classifiers Goal: eliminate false positives with few false negatives in early stages Advantages: Allows use of large set of negative training examples Improves classification speed Dangers: cannot recover from false negatives Stage 1 Sound Clip Stage 2 Stage 3 Pass Fail Pass (5%) Pass (2%) Pass (0.005%) Fail Fail FailResults: Classification Error: Results: Classification ErrorResults: ROC curves: Results: ROC curves Note: to approximate negative error rate divide FP by 25,000 Results: Anecdotal: Results: Anecdotal Gunshots Female Laugh Male Laugh Swords Scream You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
Sound Detection Dario Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 394 Category: Education License: All Rights Reserved Like it (1) Dislike it (0) Added: January 17, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Sound Detection: Sound Detection Derek Hoiem Rahul Sukthankar (mentor) August 24, 2004Objective: Objective Learn model of sound object from few (10-20) examples and distinguish from all other sounds Examples of sound classes: Gunshots, screams, laughter, car horns, meow, dog bark, etcApplications: Applications “Tell me if you hear a gunshot.” (monitoring) “Get me video clips containing dogs barking.” (search and retrieval) “What’s going on?” (scene understanding) Why its difficult: Why its difficult Sound classes have large variations Sounds are often ambiguous without context Overlaid “noise” obscures sound Sound or not?: Sound or not? Car horn Laser gun Dog bark Which of these sounds are not from their named classes?Previous work: Previous work Sound Classification (Wold 1996, Casey 2001, etc) Categorize short sound clips Reasonable accuracy (5-20% error) Sound Detection (Defaux 2000, Piamsa-nga 1999) Localize and recognize sound objects in long clips Poor performance or assumption of unrealistic conditions (e.g., very quiet background) Detection via Windowed Search: Detection via Windowed Search Long Track Break audio track into short overlapping short clips Clip Classifier Independently classify short clips as object or non-object Return locations of detected sound objectRepresentation: Representation meows phone rings Raw RepresentationClassification Features: Classification Features Diverse feature set: Different sound classes are distinctive in different ways means and standard deviations of power at different frequencies Band-width, peaks, loudness, etc. 138 features in all Classification by Decision Trees: Classification by Decision Trees Try to find simple rules that discriminate object from non-object Each decision is based on a threshold of a feature value Assign confidence based on likelihood of data for object and non-object classes at each leaf node Decision nodes Leaf NodesBoosted Trees: Boosted Trees Problem: One decision tree by itself may not be a great classifier Solution: Use several trees, with each one focusing on the mistakes of previously learned trees Adaboost: Weight training data uniformly Learn a decision tree classifier on weighted data Re-weight data giving more weight to incorrectly classified examples Final classification based on linear combination of confidences from all learned decision trees Examples of Decision Trees: Examples of Decision Trees Low percentage of power in low frequencies in mid-time of sound Very high power amplitude range Meow Gunshot High power amplitude range More complex tree that focuses on examples misclassified by tree above GunshotCascade of Classifiers: Cascade of Classifiers Goal: eliminate false positives with few false negatives in early stages Advantages: Allows use of large set of negative training examples Improves classification speed Dangers: cannot recover from false negatives Stage 1 Sound Clip Stage 2 Stage 3 Pass Fail Pass (5%) Pass (2%) Pass (0.005%) Fail Fail FailResults: Classification Error: Results: Classification ErrorResults: ROC curves: Results: ROC curves Note: to approximate negative error rate divide FP by 25,000 Results: Anecdotal: Results: Anecdotal Gunshots Female Laugh Male Laugh Swords Scream