Multi-label Ferns for Efficient Recognition of Musical Instruments in Recordings

In this paper we introduce multi-label ferns, and apply this technique for automatic classification of musical instruments in audio recordings. We compare the performance of our proposed method to a set of binary random ferns, using jazz recordings as input data. Our main result is obtaining much faster classification and higher F-score. We also achieve substantial reduction of the model size

Estimating Frequency, Amplitude And Phase Of Two Sinusoids With Very Close Frequencies

This paper presents an algorithm to estimate the parameters of two closely spaced sinusoids, providing a frequency resolution that is more than 800 times greater than that obtained by using the Discrete Fourier Transform (DFT). The strategy uses a highly optimized grid search approach to accurately estimate frequency, amplitude and phase of both sinusoids, keeping at the same time the computational effort at reasonable levels. The proposed method has three main characteristics: 1) a high frequency resolution; 2) frequency, amplitude and phase are all estimated at once using one single package; 3) it does not rely on any statistical assumption or constraint. Potential applications to this strategy include the difficult task of resolving coincident partials of instruments in musical signals.35740747Benaroya, L., Bimbot, F., Gribonval, R., Audio source separation with a single sensor (2006) IEEE Tran. on Audio, Speech, and Language Proc., 14 (1), pp. 191-199. , JanKay, S., Marple, S.L., Spectrum analysis-A modern perspective (1981) Proc. 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Acoust., Speech and Signal Processing, 37 (3), pp. 378-391. , MarchStoica, P., Sharman, C., Novel eigenanalysis method for direction estimation (1990) IEE Proceedings, 137 (1), pp. 19-26. , FebRife, D.C., Boorstyn, R.R., Multiple tone parameter estimation from discrete-time observation (1976) Bell System Technical Journal, 55 (9), pp. 1389-1410. , NovStoica, P., Händel, P., Söderström, T., Approximate maximum likelihood frequency estimation (1994) Automatica, 30 (1), pp. 131-145. , JanuaryMacleod, M.D., Joint detection and high resolution ML estimation of multiple sinusoids in noise (2001) Proc. of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 5, pp. 3065-3068. , in, Salt Lake City, MayHwang, J.-K., Chen, Y.-C., Superresolution Frequency Estimation by Alternating Notch Periodogram (1993) IEEE Transaction on Signal Processing, 41 (2), pp. 727-741. , FebruaryStoica, P., Sharman, K.C., Maximum Likelhood Methods for Directionof-Arrival Estimation (1990) IEEE Trans. Acoust., Speech and Signal Processing, 38 (7), pp. 1132-1143. , JulyBurg, J.P., Maximum Entropy Spectral Analysis (1967) Proceedings of the 37th Annual International Meeting of the Society Exploration Geophysicists, , in Oklahoma City, USADepalle, P., Helie, T., Extraction of spectral peak parameters using a short-time Fourier transform modeling and no sidelobe windows (1997) Proc. of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 19-22. , in New Paltz, USA, OctoberTufts, D., Kumaresan, R., Estimation of Frequencies of Multiple Sinusoids: Making Linear Prediction Perform Like Maximum Likelihood (1982) Proceedings of the IEEE, 70 (9), pp. 975-989. , SeptSchmidt, R.O., (1981) A signal subspace approach to multiple emitter location and spectral estimation, , Ph.D. dissertation, Stanford University, Stanford, CAWang, H., Kaveh, M., On the Performance of Signal-Subspace Processing-Part I: Narrow-Band Systems (1986) IEEE Trans. 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Acoust., Speech and Signal Processing, 38 (7), pp. 1110-1125. , JulyRoy, R., Kailath, T., ESPRIT-Estimation of Signal Parameters Via Rotational Invariance Techniques (1989) IEEE Trans. on Acoustics, Speech and Signal Processing, 37 (7). , JulyViberg, M., Ottersten, B., Sensor array processing based on subspace fitting (1991) IEEE Trans. on Signal Processing, 39 (5), pp. 1110-1121. , MayViberg, M., Ottersten, B., Kailath, T., Detection and estimation in sensor arrays using weighted subspace fitting (1991) IEEE Trans. on Signal Processing, 39 (11), pp. 2436-2449. , NovWilkes, D.M., Cadzow, J.A., The effects of phase on high-resolution frequency estimators (1993) IEEE Transaction on Signal Processing, 41 (3), pp. 1319-1330. , MarchFeyh, G., Inverse Eigenvalue Problem for Sinusoidal Frequency Estimation (1997) Proc. of 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 5, pp. 3985-3988. , in Munich, Germany, AprilGe, F.-X., Zhang, H., Yang, J., Peng, Y.-N., Super-resolution frequency estimation of the sinusoidal signals with unknown lowpass envelopes (2003) Proc. of 2003 IEEE Radar Conference, p. 273. , in Huntsville, USA, MayChristensen, M.G., Jakobsson, A., Jensen, S.H., Joint High-Resolution Fundamental Frequency and Order Estimation (2007) IEEE Transactions on Audio, Speech, and Language Processing, 15 (5), pp. 1635-1644. , JulyCapon, J., High-resolution frequency-wavenumber spectrum analysis (1969) Proc. IEEE, 57, pp. 1408-1418. , AugustNishimura, S., Adaptive detection and enhancement of closely spaced sinusoids using multirate techniques (1993) Proc. of 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 3, pp. 400-403. , in Minneapolis, USA, AprilByrnes, C.I., Georgiou, T.T., Lindquist, A., A New Approach to Spectral Estimation: A Tunable High-Resolution Spectral Estimator (2000) IEEE Transactions on Signal Processing, 48 (11), pp. 3189-3205. , NovemberLambrecht, C.B., Karrakchou, M., Wavelet packets-based highresolution spectral estimation (1995) Signal Processing, 47 (2), pp. 135-144. , NovemberLi, H.-T., Djuric, P.M., A novel approach to detection of closely spaced sinusoids (1996) Signal Processing, 51 (2), pp. 93-104. , JuneO'Shea, P., A High-Resolution Spectral Analysis Algorithm for Power-System Disturbance Monitoring (2002) IEEE Transactions on Power Systems, 17 (3). , AugustHainsworth, S., Macleod, M., On Sinusoidal Parameter Estimation (2003) Proc. of the 6th International Conference on Digital Audio Effects, , in London, UK, SeptemberSarkar, I., Fam, A.T., The interlaced chirp Z transform (2006) Signal Processing, 86 (9), pp. 2221-2232. , SeptemberBesson, O., Stoica, P., Nonlinear least-squares approach to frequency estimation and detection for sinusoidal signals with arbitrary envelope (1999) Digital Signal Processing, 9 (1), pp. 45-56. , JanuaryTolonen, T., (1999) Methods for Separation of Harmonic Sound. Sources using Sinusoidal Modeling, , presented at the 106th AES Convention, paper 4958, Munich, GermanyRoy, R.H., (1987) ESPRIT-Estimation of signal parameters via rotational invariance techniques, , Ph.D. dissertation, Stanford University, Stanford, CAStoica, P., Soderstrom, T., Statistical analysis of MUSIC and ESPRIT estimates of sinusoidal frequencies (1991) Proc. International Conference on Acoustics, Speech, and Signal Processing, 5, pp. 3273-3276. , in Toronto, Canada, AprilBesson, O., Stoica, P., Analysis of MUSIC and ESPRIT frequency estimates for sinusoidal signals with lowpass envelopes (1996) IEEE Transactions on Signal Processing, 44 (9), pp. 2359-2364. , SeptemberStoica, P., Li, H., Li, J., Amplitude estimation of sinusoidal signals: Survey, new results, and an application (2000) IEEE Transactions on Signal Processing, 48 (2), pp. 338-352. , FebruaryKay, S.M., (1993) Fundamentals of Statistical Signal Processing-Estimation Theory, , Prentice-Hall Signal Processing Series, Englewood Cliffs, NJ, US

Speech/music Discriminator Based On Multiple Fundamental Frequencies Estimation [discriminador Voz/música Baseado Na Estimação De Múltiplas Freqüências Fundamentais]

This paper introduces a new technique to discriminate between music and speech. The strategy is based on the concept of multiple fundamental frequencies estimation, which provides the elements for the extraction of three features from the signal. The discrimination between speech and music is obtained by properly combining such features. The reduced number of features, together with the fact that no training phase is necessary, makes this strategy very robust to a wide range of practical conditions. The performance of the technique is analyzed taking into account the precision of the speech/music separation, the robustness face to extreme conditions, and computational effort. A comparison with previous works reveals an excellent performance under all points of view. © Copyright 2010 IEEE - All Rights Reserved.55294300Alatan, A.A., Akansu, A.N., Wolf, W., Multi-modal Dialogue Scene Detection Using Hidden Markov Models for Content-based Multimedia Indexing (2001) Kluwer Acad., Int. Journal on Multimedia Tools and Applications, 14, pp. 137-151Cao, Y., Tavanapong, W., Kim, K., Oh, J., Audio Assisted Scene Segmentation for Story Browsing (2003) Proc. of Int. Conf. on Image and Video Retrieval, Urbana-Champaign, USA, pp. 446-455Chen, L., Rizvi, S., Özsu, M.T., Incorporating Audio Cues into Dialog and Action Scene Extraction Proc. of the 15th Annual Symp. on Electronic Imaging - Storage and Retrieval for Media Databases, Santa Clara, USA, 2003Dimitrova, N., Multimedia Content Analysis and Indexing for Filtering and Retrieval Applications (1999) Special Issue on Multimedia Technologies and Informing Systems, Part I, 2, pp. 87-100Dinh, P.Q., Dorai, C., Venkatesh, S., Video Genre Categorization Using Audio Wavelet Coefficients Proc. of 5th Asian Conference on Computer Vision, Melbourne, Australia, January 2002Li, Y., Ming, W., Kuo, C.C.J., Semantic Video Content Abstraction Based on Multiple Cues Proc. of Int. Conf. on Multimedia and Expo, Tokyo, Japan, August 2001Liu, Z., Huang, J., Wang, Y., Chen, T., Audio Feature Extraction & Analysis for Scene Classification (1997) Proc. of 1997 Workshop on Multimedia Signal Processing, Princeton, pp. 343-348. , JuneMinami, K., Akutsu, A., Hamada, H., Tonomura, Y., Video Handling with Music and Speech Detection (1998) IEEE MultiMedia, 5 (3), pp. 17-25Zhang, T., Kuo, C.-C.J., Audio content analysis for online audiovisual data segmentation and classification (2001) IEEE Transactions on Speech and Audio Processing, 3 (4), pp. 441-457Beierholm, T., Baggenstoss, P.M., Speech Music Discrimination Using Class-Specific Features (2004) Proc. of Int. Conf. on Pattern Recognition, Cambridge, UK, pp. 379-382Carey, M.J., Parris, E.S., Lloyd-Thomas, H., A comparison of features for speech, music discrimination (1999) Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Phoenix, USA, pp. 149-152Cho, Y.-C., Choi, S., Bang, S.Y., Non-negative component parts of sound for classification Proc. IEEE Int. Symp. Signal Processing and Information Technology, Darmstadt, Germany, 2003El-Maleh, K., Klein, M., Petrucci, G., Kabal, P., Speech/Music Discrimination for Multimedia Applications (2000) Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, Istanbul, Turkey, pp. 2445-2448Harb, H., Chen, L., Robust Speech/Music Discrimination Using Spectrum's First Order Statistics and Neural Networks Proc. of the IEEE Int. Symposium on Signal Processing and Its Applications, Paris, France, July 2003Jarina, R., O'Connor, N., Marlow, S., Rhythm Detection for Speech-Music Discrimination in MPEG Compressed Domain (2002) Proc. of the IEEE Int. Conf. on Digital Signal Processing, Santorini, Greece, pp. 129-132Lu, L., Zhang, H.J., Jiang, H., Content Analysis for Audio Classification and Segmentation (2002) IEEE Transactions on Speech and Audio Processing, 10 (7), pp. 504-516Saunders, J., Real-Time Discrimination of Broadcast Speech/Music (1996) Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Atlanta, pp. 993-996Scheirer, E., Slaney, M., Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator (1997) Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Munich, Germany, pp. 1331-1334Wang, P., Cai, R., Yang, S.-Q., A Hybrid Approach to News Video Classification with Multi-modal Features (2003) Proc. of Int. Conf. on Information, Communications & Signal Processing, Singapore, pp. 787-791Williams, G., Ellis, D., Speech/music discrimination based on posterior probability features Proc. of European Conf. on Speech Communication and Technology, Budapest, Hungary, 1999Tolonen, T., Karjalainen, M., A Computationally Efficient Multipitch Analysis Model (2000) IEEE Transactions on Speech and Audio Processing, 8 (6), pp. 708-716Tzanetakis, G., Cook, P., Musical Genre Classification of Audio Signals (2002) IEEE Transactions on Speech and Audio Processing, 10 (5), pp. 293-30

Musical Instrument Classification Using Individual Partials

In a musical signals, the spectral and temporal contents of instruments often overlap. If the number of channels is at least the same as the number of instruments, it is possible to apply statistical tools to highlight the characteristics of each instrument, making their identification possible. However, in the underdetermined case, in which there are fewer channels than sources, the task becomes challenging. One possible way to solve this problem is to seek for regions in the time and/or frequency domains in which the content of a given instrument appears isolated. The strategy presented in this paper explores the spectral disjointness among instruments by identifying isolated partials, from which a number of features are extracted. The information contained in those features, in turn, is used to infer which instrument is more likely to have generated that partial. Hence, the only condition for the method to work is that at least one isolated partial exists for each instrument somewhere in the signal. If several isolated partials are available, the results are summarized into a single, more accurate classification. Experimental results using 25 instruments demonstrate the good discrimination capabilities of the method. © 2010 IEEE.191111122Agostini, G., Longari, M., Pollastri, E., Musical instrument timbres classification with spectral features (2003) EURASIP J. Appl. Signal Process., 2003, pp. 5-14Benetos, E., Kotti, M., Kotropoulos, C., Musical instrument classification using non-negative matrix factorization algorithms and subset feature selection (2006) Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp. 221-224Chetry, N., Sandler, M., Linear predictive models for musical instrument identification (2009) Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp. 173-176Eronen, A., Klapuri, A., Musical instrument recognition using cepstral coefficients and temporal features (2000) Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp. 753-756Essid, S., Richard, G., David, B., Musical instrument recognition by pairwise classification strategies (2006) IEEE Trans. Audio, Speech, Lang. Process., 14 (4), pp. 1401-1412. , JulFanelli, A.M., Caponetti, L., Castellano, G., Buscicchio, C.A., Content-based recognition of musical instruments (2004) Proc. IEEE Int. Symp. Signal Process. Inf. Tech., pp. 361-364Ihara, M., Maeda, S.-I., Ishii, S., Instrument identification in monophonic music using spectral information (2007) Proc. IEEE Int. Symp. Signal Process. Inf. Tech., pp. 595-599Joder, C., Essid, S., Richard, G., Temporal integration for audio classification with application to musical instrument classification (2009) IEEE Trans. Audio, Speech, Lang. Process., 17 (1), pp. 174-186. , JanKrishna, A.G., Sreenivas, T.V., Music instrument recognition: From isolated notes to solo phrases (2004) Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp. 265-268Martin, K.D., Kim, Y.E., Musical instrument identification: A pattern-recognition approach (1998) Proc. Meeting Acoust. Soc. Amer.Pruysers, C., Schnapp, J., Kaminskyj, I., Wavelet analysis in musical instrument sound classification (1998) Proc. Int. Symp. Signal Process. Applicat.Brown, J.C., Computer identification of musical instruments using pattern recognition with cepstral coefficients as features (1999) J. Acoust. Soc. Amer., 105, pp. 1933-1941Brown, J.C., Houix, O., McAdams, S., Feature dependence in the automatic identification of musical woodwind instruments (2001) J. Acoust. Soc. Amer., 109, pp. 1064-1072Fragoulis, D., Papaodysseus, C., Exarhos, M., Roussopoulos, G., Panagopoulos, T., Kamarotos, D., Automated classification of piano-guitar notes (2006) IEEE Trans. Audio, Speech, Lang. Process., 14 (3), pp. 1040-1050. , MayBurred, J.J., Robel, A., Sikora, T., Polyphonic musical instrument recognition based on a dynamic model of the spectral envelope (2009) Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp. 173-176Jincahitra, P., Polyphonic instrument identification using independent subspace analysis (2004) Proc. IEEE Int. Conf. Multimedia Expo, pp. 1211-1214Kashino, K., Murase, H., A sound source identification system for ensemble music based on template adaptation and music stream extraction (1999) Speech Commun., 27, pp. 337-349Kitahara, T., Goto, M., Komatani, K., Ogata, T., Okuno, H.G., Instrument identification in polyphonic music: Feature weighting to minimize influence of sound overlaps (2007) EURASIP J. Appl. Signal Process., 2007, pp. 1-15Martins, L.G., Burred, J.J., Tzanetakis, G., Lagrange, M., Polyphonic instrument recognition using spectral clustering (2007) Proc. Int. Conf. Music Inf. RetrievalVincent, E., Rodet, X., Instrument identification in solo and ensemble music using independent subspace analysis (2004) Proc. Int. Conf. Music Inf. Retrieval, pp. 576-581Eggink, J., Brown, G.J., Application of missing feature theory to the recognition of musical instruments in polyphonic audio (2003) Proc. Int. Conf. Music Inf. Retrieval, pp. 1-7Eggink, J., Brown, G.J., Instrument recognition in accompanied sonatas and concertos (2004) Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp. 217-220Leveau, P., Sodoyer, D., Daudet, L., Automatic instrument recognition in a polyphonic mixture using sparse representations (2007) Proc. Int. Conf. Music Inf. RetrievalEssid, S., Richard, G., David, B., Instrument recognition in polyphonic music based on automatic taxonomies (2006) IEEE Trans. Audio, Speech, Lang. Process., 14 (1), pp. 68-80. , JanSomerville, P., Uitdenbogerd, A.L., Multitimbral musical instrument classification (2008) Proc. Int. Symp. Comp. Science Applic., pp. 269-274Eggink, J., Brown, G.J., A missing feature approach to instrument identification in polyphonic music (2003) Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp. 553-556Kostek, B., Musical instrument classification and duet analysis employing music information retrieval techniques (2004) Proc. IEEE, 92 (4), pp. 712-729. , AprThornburg, H., Leistikow, R.J., Berger, J., Melody extraction and musical onset detection via probabilistic models of framewise STFT peak data (2007) IEEE Trans. Audio, Speech, Lang. Process., 15 (4), pp. 1257-1272. , MayHu, G., Wang, D., Auditory segmentation based on onset and offset analysis (2007) IEEE Trans. Audio, Speech, Lang. Process., 15 (2), pp. 396-405. , FebZhou, R., Mattavelli, M., Zoia, G., Music onset detection based on resonator time frequency image (2008) IEEE Trans. Audio, Speech, Lang. Process., 16, pp. 1685-1695Barbedo, J.G.A., Lopes, A., Wolfe, P.J., Empirical methods to determine the number of sources in single-channel musical signals (2009) IEEE Trans. Audio, Speech, Lang. Process., 17 (8), pp. 1435-1444. , NovBarbedo, J.G.A., Lopes, A., Wolfe, P.J., High time-resolution estimation of multiple fundamental frequencies (2007) Proc. Int. Conf. Music Inf. Retrieval, pp. 399-403Rauhala, J., Lehtonen, H.-M., Valimaki, V., Fast automatic inharmonicity estimation algorithm (2007) J. Acoust. Soc. Amer., 121, pp. EL184-EL189Brown, J.C., Frequency ratios of spectral components of musical sounds (1996) J. Acoust. Soc. Amer., 99, pp. 1210-1218Oppenheim, A.V., Schafer, R.W., (1975) Digital Signal Processing, , Englewood Cliffs, NJ: Prentice-HallSmith III, J.O., Spectral Audio Signal Processing, , https://ccrma.stanford.edu/~jos/sasp, Online. AvailableEronen, A., Comparison of features for music instrument recognition (2001) Proc. IEEE Workshop Applicat. Signal Process Audio Acoust., pp. 19-22Every, M.R., Discriminating between pitched sources in music audio (2008) IEEE Trans. Audio, Speech, Lang. Process., 16 (2), pp. 267-277. , FebDeng, J.D., Simmermacher, C., Cranefield, S., A study on feature analysis for musical instrument classification (2008) IEEE Trans. Syst., Man. Cybern. B: Cybern., 38 (2), pp. 429-438. , AprGoto, M., Development of the RWC music database (2004) Proc. Int. Cong. Acoustics, pp. 553-556http://theremin.music.uiowa.edu, University of Iowa Musical Instrument Samples Database. Online. AvailableElliott, C., Attacks and releases as factors in instrument identification (1975) J. Res. Music Educat., 23, pp. 35-40Kendall, R.A., The role of acoustic signal partitions in listener categorization of musical phrases (1986) Music Percep., 4, pp. 185-214Martin, K.D., (1999) Sound-source Recognition: Atheory and Computational Model, , Ph. D. dissertation, Mass. Inst. of Technol., Cambridge, U. 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A New Strategy For Objective Estimation Of The Quality Of Audio Signals [uma Nova Estratégia Para A Estimação Objetiva Da Qualidade De Sinais De áudio]

This paper presents a new strategy for assessment of audio signal quality. The resulting method, named Objective Measure of Audio Quality, includes some of the most effective features found in the current methods of audio assessment, as well new techniques resulting from the identification and study of the major limitations of such methods. The performance of the new strategy is compared to that one achieved by PEAQ (Perceptual Evaluation of Audio Quality), currently adopted as standard by International Telecommunication Union (ITU).23162167Thiede, T.V., Kabot, E., A New Perceptual Quality Measure for Bit Rate Reduced Audio (1996) Contribution to the 100th AES Convention, , preprint 4280, CopenhagenBrandenburg, K., Evaluation of Quality for Audio Encoding at Low Bit Rates (1987) Contribution to the 82nd AES Convention, , preprint 2433, LondonBeerends, J.G., Stemerdink, J.A., A Perceptual Audio Quality Measure Based on a Psychoacoustic Sound Representation (1992) J. Audio Eng. Soc., 40, pp. 963-978. , DecPaillard, B., Mabilleau, P., Morisette, S., Soumagne, J., Perceval: Perceptual Evaluation of the Quality of Audio Signals (1992) J. Audio Eng. Soc., 40, pp. 21-31. , JanColomes, C., Lever, M., Rault, J.B., Dehery, Y.F., A Perceptual Model Applied to Audio Bit-Rate Reduction (1995) J. Audio Eng. Soc., 43, pp. 233-240. , April(1998) Method for Objective Measurements of Perceived Audio Quality, , ITU-R Recommendation BS.1387-1Thiede, T.V., (1999) Perceptual Audio Quality Assessment Using a Non-Linear Filter Bank, , Ph.D. Thesis, Technical University of BerlinKohonen, T., (1997) Self-Organizing Maps, , 2nd Edition, SpringerBarbedo, J.G.A., Lopes, A., (2003) Medida Objetiva Da Qualidade de Áudio, , Relatório Técnico, Fapesp, julBarbedo, J.G.A., Lopes, A., (2004) On the Vectorization of FIR Filterbanks, , submitted to the EUSIPCOBarbedo, J.G.A., Lopes, A., (2004) On the Vectorization of IIR Filterbanks, , submitted to the EUSIPCOZwicker, E., Fastl, H., (1990) Psychoacoustics, Facts and Models, , Springer Verlag, BerlinBeerends, J.G., Van Den Brink, W.A.C., The Role of Informational Masking and Perceptual Streaming in the Measurement of Music Codec Quality (1996) Contribution to the 100th Convention of the Audio Engineering Society, , Preprint 4176, Copenhagen, MayHaykin, S., (1999) Neural Networks - A Comprehensive Foundation, , New Jersey: Prentice Hal

Drying and storage of Eugenia involucrata DC. seeds Secagem e armazenamento de sementes de Eugenia involucrata DC.

The physiological quality of seeds of native species is important to produce healthy saplings and therefore guarantee the success of programs to recover disturbed vegetation. This reinforces the necessity for investigating the physiological quality of those seeds. To evaluate the effects of different drying rates on the germination, moisture content and storability of Eugenia involucrata diaspores, mature fruits collected at Mogi Guaçu, SP, Brazil had their epi- and mesocarps removed by washing and were dried at 30, 40 or 50&ordm;C until their water content was reduced from 57% (fresh diaspores) to 13% (final drying), totaling six drying levels. In a second experiment, diaspores had their moisture content reduced from 57% to 49%, at 30&ordm;C, totaling six drying levels (0h, 1h, 2h, 3h, 4h and 5h), and were kept for 180 days in plastic bags under cold storage. The drying rate had no effect on tolerance to desiccation by E. involucrata diaspores; water contents lower than 51% decreased both germinability and storability. Diaspores can be stored for up to 180 days as long as their water content is reduced to 53% and they are kept inside plastic bags under cold storage.<br>O uso de sementes de espécies nativas de alta qualidade é fundamental nos programas de recomposição vegetal, o que fortalece a necessidade de se investigar o potencial fisiológico das mesmas. Esta pesquisa objetivou avaliar os efeitos da velocidade de secagem dos diásporos de Eugenia involucrata sobre a sua germinação e vigor, bem como as relações entre teor de água e capacidade de armazenamento. Foram colhidos frutos maduros em pomar instalado em Mogi Guaçu, SP (22&ordm;15-16'S, 47&ordm;8-12'W), que tiveram seu epicarpo e mesocarpo removidos por lavagem. A seguir, os diásporos (semente + endocarpo) foram submetidos a secagem controlada a 30, 40 e 50&ordm;C, com reduções progressivas do teor de água inicial de 57% para até 13%, obtendo-se seis níveis de secagem em cada temperatura. Em um segundo experimento, a secagem foi realizada a 30&ordm;C por 0h (controle), 1h, 2h, 3h, 4h e 5h, tendo atingido, neste último período, 49% de água. Neste experimento, os diásporos foram avaliados quanto à germinação até 180 dias de armazenamento em sacos plásticos em câmara fria (8 + 2&ordm;C). A velocidade de secagem não alterou a sensibilidade dos diásporos à dessecação. A redução da umidade para valores inferiores a 51% prejudica a capacidade germinativa e o potencial de armazenamento. A redução do teor de água para 53% permite conservação dos diásporos de E. involucrata por até 180 dias, sob condições de câmara fria e em embalagens plásticas