[5] F 16.1% MFCC NMF D-CASE 17 [5] NMF NMF 3. [5] 1 NMF Deep Neural Network(DNN) FUSION 3.1 NMF NMF [12] S W H 1 Fig. 1 Our aoustic event detect

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "[5] F 16.1% MFCC NMF D-CASE 17 [5] NMF NMF 3. [5] 1 NMF Deep Neural Network(DNN) FUSION 3.1 NMF NMF [12] S W H 1 Fig. 1 Our aoustic event detect"

Κόρη Γιαννακόπουλος
6 χρόνια πριν
Προβολές:

1 NMF 1 1,a) 1 AED NMF DNN IEEE D-CASE % DNN NMF 1. Computational Auditory Scene Analysis: CASA [1] [2] [3] [4] [5] Non-negative Matrxi Factorization (NMF) NMF 2. CASA IEEE 1 Dept. Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Aichi , Japan a) kyama@tut.jp D-CASE [6] D-CASE 2 OL (Office Live) OS (Office Synthetic) Vuegen [8] MFCC (Mel-Frequency Cepstral Coefficients) GMM (Gaussian Mixture Model) OL 43.4% OS 13.5% F Gemmeke [9] Exemplar-based NMF OL 31.4% OS 21.3% F 2 OL 1

2 [5] F 16.1% MFCC NMF D-CASE 17 [5] NMF NMF 3. [5] 1 NMF Deep Neural Network(DNN) FUSION 3.1 NMF NMF [12] S W H 1 Fig. 1 Our aoustic event detection system S Ŝ = WH I(i =1, 2,,I) J(j =1, 2,,J) K(k =1, 2,,K) S Ŝ I J W I K H K J W n W W =[W 1,W 2,...,W N ] N W n H n W H S N n W nh n (n =1, 2,,N) N S n S n S ˆ W n H n n = S N m W (1) mh m [13] VQ ( ) NMF NMF W VQ VQ 2

3 4 Fig. 4 Score fusion 2 (1) Fig. 2 Shared bases (1) number of bases for each class is variable j j i C j N O i M i X i = exp(o i) M j exp(o j ) (3) 3 (2) Fig. 3 Shared bases (2) number of bases for each class is constant W (1) 2 (2) 3 2 Komatsu [14] 3.2 DNN j j O j N O j = W ij h i + C j (2) i=1 h i i W ij O i X i 4 MFCC FBANK 2 DNN 2 FUSION 3.3 (a) DNN NMF (1) DNN DNN 5 DNN (sil) X sil DNN O j DNN (3) (2) OL 3

5 Fig. 5 Acoustic event detection and threshold 9 DNN DNN 1 (b) NMF NMF NMF 4. [5] NMF 6 4 4 4 NMF 5. IEEE D-CASE [6] TASK2 OS 16 (alert, clearthroat, 6 Fig.

4 5 Fig. 5 Acoustic event detection and threshold 9 DNN DNN 1 (b) NMF NMF NMF 4. [5] NMF NMF 5. IEEE D-CASE [6] TASK2 OS 16 (alert, clearthroat, 6 Fig. 6 Distance between basis vectors within class and across classes cough, doorslam, drawer, keyboard, keys, knock, laughter, mouse, pageturn, pendrop, phone, printer, speech, switch) / 44.1kHz/24bit OS , , NMF NMF 20ms 10ms z 1 VQ 4 KL-divergence (1) (2) 4 5 (1) , 0.40, 0.45, , 0.45, 0.50,

5 5.1.2 (SS) [15] SS SS MFCC FBANK 20ms 10ms NMF MFCC 12 Δ, ΔΔ FBANK 45 Δ, ΔΔ DNN D-CASE SNR 10, 15, 20dB D-CASE OL DNN 3 7 MFCC 273 FBANK Rectified Linear Unit D-CASE Challenge F 10ms F F [%]=2 P recison Recall P recision + Recall P recison[%] = C E, Recall[%] = C GT (4) (5) C E GT [5], [6], [8], [9], [10] F Recall 2 Recall R overlap F Table 1 Results of revious works Baseline[6] DHV[10] GVV[9] VVK[8] F [%] NMF DNN [5] Table 2 Results for only NMF and only DNN from [5] NMF DNN MFCC FBANK FUSION R overlap [%] F [%] NMF DNN [9] NMF NMF F 14.4% NMF 3 4 NMF DNN F 2 DNN MFCC 10.3% FBANK 4.4% FUSION 41.3% F F laughter 17 alert, switch 5 4 NMF-DNN F 6. [5] NMF NMF 5

6 Table 3 3 -F - Sharing method -F-measure- - - Threshold cb size MFCC FBANK Select cb size MFCC FBANK Table 4 Comparison between conventional and proposed methods Method Feature R overlap[%] F [%] DNN[5] FUSION NMF-DNN[5] Threhold = 0.45 cb size = 8 Threhold = 0.55 cb size = 8 MFCC FBANK FUSION MFCC FBANK FUSION MFCC FBANK FUSION NMF DNN [5] DNN F 4.4% FBANK FUSION F 41.3% [9] 20% [5] FUSION 4.8% [1] A. Temko, et al., Comparison of sequence discriminant support vector machines for acoustic event classification, in Proc. IEEE ICASSP 2006, pp , 2006 [2] S. Deng, et al., Robust minimum statistic project coefficients feature for acoustic environment recognition, in Proc. IEEE ICASSP 2014, pp , 2014 [3] J. Salmon and J. P. Bello, Unsupervised feature learning for urban sound classification, in Proc. IEEE ICASSP 2015, pp , [4] R. Radhakrishnan, et al., Audio analysys for surveillance applications, in Proc IEEE WASPAA, pp , [5],, 2016, 3-P-8, pp , [6] D. Stowell, D. Giannoulis, E. Benetos, M. Lagrange, and M. D. Plumbley, Detection and classification of acousitc scenes and events, in IEEE Trans. Multimedia, vol.17, pp , [7] N. Moreau, et al., Data collection for the CHIL CLEAR 2007 evaluation campaign, in Proc. LREC 08, pp.28 30, [8] L. Vuegen, et al., An MFCC - GMM approach apploach for event detection and classification, in IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events, [Online]. Available: sceneseventschallenge/abstracts/ol/vvk.pdf [9] J. F. Gemmeke, et al., An exemplar-based NMF approach to audio event detection, in IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events, [Online]. Available: sceneseventschallenge/abstracts/ol/gvv.pdf [10] A. Diment, et al., Sound event detection for office live and office synthetic AASP challenge, in IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events, [Online]. Available: sceneseventschallenge/abstracts/ol/dhv.pdf [11] X. Zhou, et al., HMM-based acoustic event detection with AdaBoost feature selection, in Classification of Events, Activities and Relationships Evaluation and Workshop, [12] D. D. Lee, H. S. Seung, Algorithms for non-negative matrix factorization, in NIPS, pp , [13], NMF VQ, 2012, 1-P-18, pp , [14] T. Komatsu, et al., Acoustic event detection based on non-negative matrix factorization with mixtures of local dictionaries and activation aggregation, in Proc. ICASSP 2016, pp , [15] S. F. Boll, Suppression of acoustic noise in speech using spectral subtraction, in IEEE Trans. ASSP, Vol.27, No.2, pp , [16],,, Vol.68, No.2, pp.74 85,

Σχετικά έγγραφα

CSJ. Speaker clustering based on non-negative matrix factorization using i-vector-based speaker similarity

CSJ. Speaker clustering based on non-negative matrix factorization using i-vector-based speaker similarity i-vector 1 1 1 1 i-vector CSJ i-vector Speaker clustering based on non-negative matrix factorization using i-vector-based speaker similarity Fukuchi Yusuke 1 Tawara Naohiro 1 Ogawa Tetsuji 1 Kobayashi