spectral

问题 I ' am working on audio file and need to use spafe library for lfcc, lpc... and i install the library as mentionned in the site : https://spafe.readthedocs.io/en/latest/ But when i try to extract some features , like lfcc, mfcc, lpc, i have import error par example when i use this code : import scipy.io.wavfile import spafe.utils.vis as vis from spafe.features.mfcc import lfcc i have this error : ImportError: cannot import name 'lfcc' I don't undestand because i can import spafe, i have all

问题 I ' am working on audio file and need to use spafe library for lfcc, lpc... and i install the library as mentionned in the site : https://spafe.readthedocs.io/en/latest/ But when i try to extract some features , like lfcc, mfcc, lpc, i have import error par example when i use this code : import scipy.io.wavfile import spafe.utils.vis as vis from spafe.features.mfcc import lfcc i have this error : ImportError: cannot import name 'lfcc' I don't undestand because i can import spafe, i have all

问题 BACKGROUND Per the Coastal Information Data Program (CDIP), they are generating a spectral heat/intensity map for wave swell at http://cdip.ucsd.edu/?nav=recent&sub=observed&units=metric&tz=UTC&pub=public&map_stati=1,2,3&stn=100&stream=p1&xitem=dir_spectrum. This is dynamically generated with data containing energy density, duration (in seconds) & direction (in degrees with 180 degrees representing south). DATA SAMPLE Here's an explanation of the data: http://cdip.ucsd.edu/data_access/MEM

问题 I updated my MacBook to Mavericks, reinstalled Macports and all Python 2.7 modules I usually use. While running Python I get the following messages: when importing mlab: from mayavi import lab (process:1146): Gtk-WARNING **: Locale not supported by C library. Using the fallback 'C' locale. when running a mlab command such as mlab.mesh(), the display window opens, shows no content and freezes. I don't get this message while importing spectral, but I get it when running view_cube() the display

问题 i'm new to Android and DSP. i'm implementing the Spectral Subtraction algorithm thanks my goal is to use this algorithm over a phone call stream buffer i'm trying to figure how to implement this code from the Matlab implementation of Spectral Subtraction i am using. Matalb code: function Seg=segment(signal,W,SP,Window) % SEGMENT chops a signal to overlapping windowed segments % A= SEGMENT(X,W,SP,WIN) returns a matrix which its columns are segmented % and windowed frames of the input one

问题 I'm having multiple data frames where the first column (in the end filled with NA's) is the wavenumber and the other columns are my variables of the specific wavenumber for multiple observations. Is there a possibility to plot the columns in a way that my first column holds the variables for the x-axis and the other are plotted into one big plot with their respective y-values? I already tried "matplot" (resulting in "numbers" instead of points), matplot(df[,1],df[,3:5],xlab = "Wavelength [nm]

问题 I have a 3000x50 feature vector matrix. I obtained a similarity matrix for this using sklearn.metrics.pairwise_distances as 'Similarity_Matrix'. Now I used networkx to create a graph using the similarity matrix generated in the previous step as G=nx.from_numpy_matrix(Similarity_Matrix) . I want to perform spectral clustering on this graph G now but several google searches have failed to provide a decent example of scikit learn spectral clustering on this graph :( The official documentation

问题 I'd like to cluster a graph in python using spectral clustering. Spectral clustering is a more general technique which can be applied not only to graphs, but also images, or any sort of data, however, it's considered an exceptional graph clustering technique. Sadly, I can't find examples of spectral clustering graphs in python online. Scikit Learn has two spectral clustering methods documented: SpectralClustering and spectral_clustering which seem like they're not aliases. Both of those

问题 I have a bunch of sentences and I want to cluster them using scikit-learn spectral clustering. I've run the code and get the results with no problem. But, every time I run it I get different results. I know this is the problem with initiation but I don't know how to fix it. This is my a part of my code that runs on sentences: vectorizer = TfidfVectorizer(norm='l2',sublinear_tf=True,tokenizer=tokenize,stop_words='english',charset_error="ignore",ngram_range=(1, 5),min_df=1) X = vectorizer.fit