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multisource environments. In: Proceedings Machine Listening in Multisource Environments,
CHiME 2011, Satellite Workshop of Interspeech 2011, pp. 24-29. ISCA, Florence, Sept 2011
6. Weninger, F., Wöllmer, M., Geiger, J., Schuller, B., Gemmeke, J., Hurmalainen, A., Virtanen,
T., Rigoll, G.: Non-negative matrix factorization for highly noise-robust asr: to enhance or to
recognize? In: Proceedings 37th IEEE International Conference on Acoustics, Speech, and
Signal Processing, ICASSP 2012, pp. 4681-4684. IEEE, Kyoto, March 2012
7. Weninger, F., Feliu, J., Schuller, B.: Supervised and semi-supervised supression of background
music in monaural speech recordings. In: Proceedings 37th IEEE International Conference
on Acoustics, Speech, and Signal Processing, ICASSP 2012, pp. 61-64. IEEE, Kyoto, March
2012
8. Weninger, F., Amir, N., Amir, O., Ronen, I., Eyben, F., Schuller, B.: Robust feature extraction
for automatic recognition of vibrato singing in recorded polyphonic music. In: Proceedings
37th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP
2012, pp. 85-88. IEEE, Kyoto, March 2012
9. Joder, C., Weninger, F., Eyben, F., Virette, D., Schuller, B.: Real-time speech separation by
semi-supervised nonnegative matrix factorization. In: Theis, F.J., Cichocki, A., Yeredor, A.,
Zibulevsky, M. (eds.) Proceedings 10th International Conference on Latent Variable Analysis
and Signal Separation (LVA/ICA 2012). Lecture Notes in Computer Science, vol. 7191, pp.
322-329. Springer, Tel Aviv (2012)
10. Batliner, A., Steidl, S., Schuller, B., Seppi, D., Laskowski, K., Vogt, T., Devillers, L., Vidrascu,
L., Amir, N., Kessous, L., Aharonson, V.: Combining efforts for improving automatic classifi-
cation of emotional user states. In: Proceedings 5th Slovenian and 1st International Language
Technologies Conference, ISLTC2006, pp. 240-245. Slovenian Language Technologies Soci-
ety, Ljubljana, Oct 2006
11. Schuller, B., Wimmer, M., Mösenlechner, L., Kern, C., Arsic, D., Rigoll, G.: Brute-forcing
hierarchical functionals for paralinguistics: a waste of feature space? In: Proceedings 33rd
IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2008,
pp. 4501-4504. IEEE, Las Vegas, April 2008
12. Schuller, B.: The computational paralinguistics challenge. IEEE Signal Process. Mag. 29 (4),
97-101 (2012)
13. Schuller, B., Weninger, F., Wöllmer, M., Sun, Y., Rigoll, G.: Non-negative matrix factor-
ization as noise-robust feature extractor for speech recognition. In: Proceedings 35th IEEE
International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, pp.
4562-4565. IEEE, Dallas, March 2010
14. Schuller, B., Weninger, F.: Discrimination of speech and non-linguistic vocalizations by
non-negative matrix factorization. In: Proceedings 35th IEEE International Conference on
Acoustics, Speech, and Signal Processing, ICASSP 2010, pp. 5054-5057. IEEE, Dallas,
March 2010
15. Weninger, F., Schuller, B., Batliner, A., Steidl, S., Seppi, D.: Recognition of non-prototypical
emotions in reverberated and noisy speech by non-negative matrix factorization. EURASIP
J. Adv. Signal Process. Article ID 838790 , 16 (2011). Special issue on emotion and mental
state recognition from speech
16. Weninger, F., Schuller, B., Wöllmer, M., Rigoll, G.: Localization of non-linguistic events in
spontaneous speech by non-negative matrix factorization and long short-term memory. In:
Proceedings 36th IEEE International Conference on Acoustics, Speech, and Signal Process-
ing, ICASSP 2011, pp. 5840-5843. IEEE, Prague, May 2011
17. Schuller, B., Gollan, B.: Music theoretic and perception-based features for audio key deter-
mination. J. New Music Res. 41 (2), 175-193 (2012)
18. Wöllmer, M., Eyben, F., Graves, A., Schuller, B., Rigoll, G.: A tandem blstm-dbn architecture
for keyword spotting with enhanced context modeling. In: Proceedings ISCA Tutorial and
ResearchWorkshop on Non-Linear Speech Processing, NOLISP 2009, pp. 9. ISCA, Vic, June
2009
19. Wöllmer, M., Eyben, F., Schuller, B., Douglas-Cowie, E., Cowie, R.: Data-driven clustering
in emotional space for affect recognition using discriminatively trained lstm networks. In:
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