analysis

I'm trying to use Python to retrieve the dominant frequencies of a live audio input. For the moment I am experimenting using the audio stream my Laptop's built in microphone, but when testing the following code, I am getting very poor results. # Read from Mic Input and find the freq's import pyaudio import numpy as np import bge import wave chunk = 2048 # use a Blackman window window = np.blackman(chunk) # open stream FORMAT = pyaudio.paInt16 CHANNELS = 1 RATE = 1920 p = pyaudio.PyAudio() myStream = p.open(format = FORMAT, channels = CHANNELS, rate = RATE, input = True, frames_per_buffer =

I'm looking for a java driven solution to a requirement for analysing sentences to log whether a key word was used positively or negatively. Ie The key word might be 'cabbages' and the sentence:- 'I like cabbages but not peas' And I'd like a java text analyser of some kind to log this as positive. Can the lucene (Hibernate-Search) libraries be utilized to for this? Any thoughts? You're looking for "sentiment analysis". One possibility is LingPipe , who kindly link to their competitors also . Jeff Dalton also has a great list of natural language processing tools in his blog . I doubt there's

Is there prior open-source work done in the field of 'Audio analysis' to detect human-voice (say in spite of some background noise), determine speaker's gender, possibly determine no. of speakers, age of speaker(s), and the emotion of speakers? My hunch is that the speech recognition software like CMU Sphinx could be a good place to start, but if there's something better, it'd be great. I'm a graduate student doing speech recognition research. These are open research problems, and, unfortunately, I'm not aware of open-source packages that can do these things out of the box. If you have some

I'm working on a project where I need to analyze a page of text and collections of pages of text to determine dominant words. I'd like to know if there is a library (prefer c# or java) that will handle the heavy lifting for me. If not, is there an algorithm or multiple that would achieve my goals below. What I want to do is similar to word clouds built from a url or rss feed that you find on the web, except I don't want the visualization. They are used all the time for analyzing the presidential candidate speeches to see what the theme or most used words are. The complication, is that I need

I have a file that I want to extract dates from, it's a HTML source file so it's full of code and phrases I don't need. I need to extract every instance of a date that's wrapped in a specific HTML tag: abbr title="((this is the text I need))" data-utime=" What's the easiest way to achieve this? Dick Kusleika If you're using Excel VBA, set a reference (Tools - References) to the MSHTML library (entitled Microsoft HTML Object Library in the reference menu) Sub ScrapeDateAbbr() Dim hDoc As MSHTML.HTMLDocument Dim hElem As MSHTML.HTMLGenericElement Dim sFile As String, lFile As Long Dim sHtml As

I'm building an analyzer for a series of strings. I need to check how much each line is indented (either by tabs or by spaces). Each line is just a string in a text editor. How do I check by how much a string is indented? Or rather, maybe I could check how much whitespace or \t are before a string, but I'm unsure of how. Gizmo To count the number of spaces at the beginning of a string you could do a comparison between the left stripped (whitespace removed) string and the original: a = " indented string" leading_spaces = len(a) - len(a.lstrip()) print(leading_spaces) # >>> 4 Tab indent is